From d9a153b6f4c1f542fca862b34b88a1cd6ba85dd9 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Sat, 8 Jul 2023 17:48:21 +0100 Subject: [PATCH 01/92] first draft --- joss_paper/paper.bib | 0 joss_paper/paper.md | 71 ++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 71 insertions(+) create mode 100644 joss_paper/paper.bib create mode 100644 joss_paper/paper.md diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib new file mode 100644 index 00000000..e69de29b diff --git a/joss_paper/paper.md b/joss_paper/paper.md new file mode 100644 index 00000000..2d378935 --- /dev/null +++ b/joss_paper/paper.md @@ -0,0 +1,71 @@ +--- +title: 'Alchemlyb: The Simple Alchemistry Library' +tags: + - Python + - alchemistry + - molecular dynamics + - free energy +authors: + - name: Zhiyi Wu + orcid: 0000-0002-7615-7851 + equal-contrib: true + affiliation: 1 + - name: David Dotson + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + affiliation: 2 + - name: Author with no affiliation + corresponding: true # (This is how to denote the corresponding author) + affiliation: 3 +affiliations: + - name: Exscientia, Oxford, UK. + index: 1 + - name: Institution Name, Country + index: 2 + +date: 99 August 2023 +bibliography: paper.bib + +--- + +# Summary + +alchemlyb is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from Molecular Dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation, using various estimators to derive free energy estimates, and finally providing quality analysis tools for data convergence checking. + +A distinctive attribute of alchemlyb is its streamlined, end-to-end analysis process reminiscent of the now-discontinued alchemical analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. + +# Statement of need + +In the pharmaceutical sector, computational chemistry techniques, particularly relative/absolute binding free energy calculations, are regularly employed to rank potential drug compounds based on their protein-binding affinity. These calculations produce free energy data, which alchemlyb expertly processes to offer free energy estimates. These estimates provide critical insights into the binding affinity at various stages of drug discovery, such as hit identification and lead optimization. alchemlyb's unique capacity to cater to this need has cemented its role as an invaluable asset in computational chemistry. + +Moreover, within the realm of computational research, different MD engines, including GROMACS, AMBER, OpenMM, and NAMD, have their distinct sets of tools for conducting free energy calculations. This diversity complicates the research process, as data from different engines necessitate unique processing and analysis methods. + +The solution to this complication comes in the form of alchemlyb, providing a unified, engine-agnostic analysis workflow. This allows for consistent analysis of free energy data from different MD engines, making it possible for researchers to compare and combine results from various engines in a more streamlined manner. + +# Citations + +Citations to entries in paper.bib should be in +[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) +format. + +If you want to cite a software repository URL (e.g. something on GitHub without a preferred +citation) then you can do it with the example BibTeX entry below for @fidgit. + +For a quick reference, the following citation commands can be used: +- `@author:2001` -> "Author et al. (2001)" +- `[@author:2001]` -> "(Author et al., 2001)" +- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" + +# Figures + +Figures can be included like this: +![Caption for example figure.\label{fig:example}](figure.png) +and referenced from text using \autoref{fig:example}. + +Figure sizes can be customized by adding an optional second parameter: +![Caption for example figure.](figure.png){ width=20% } + +# Acknowledgements + +We acknowledge contributions from XXXXX during the genesis of this project. + +# References \ No newline at end of file From 7d697754c7e5200e9d3067779b927f04a91da469 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Fri, 29 Dec 2023 20:16:46 +0000 Subject: [PATCH 02/92] Test render --- .github/workflows/draft-pdf.yml | 23 +++++ joss_paper/paper.bib | 155 ++++++++++++++++++++++++++++++++ joss_paper/paper.md | 42 ++++----- 3 files changed, 199 insertions(+), 21 deletions(-) create mode 100644 .github/workflows/draft-pdf.yml diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 00000000..9612161e --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: joss_paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper.pdf diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index e69de29b..f0be9a25 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -0,0 +1,155 @@ +@article{bennett1976efficient, + title={Efficient estimation of free energy differences from Monte Carlo data}, + author={Bennett, C. H.}, + journal={Journal of Computational Physics}, + volume={22}, + number={2}, + pages={245-268}, + year={1976}, +} + +@article{case2014ff14sb, + title={The FF14SB force field}, + author={Case, D. and Babin, V. and Berryman, J. and Betz, R. and Cai, Q. and Cerutti, D. and et al.}, + journal={Amber}, + volume={14}, + pages={29-31}, + year={2014}, +} + +@article{chodera2007use, + title={Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations}, + author={Chodera, J. D. and Swope, W. C. and Pitera, J. W. and Seok, C. and Dill, K. A.}, + journal={J Chem Theory Comput}, + volume={3}, + number={1}, + pages={26-41}, + year={2007}, + doi={10.1021/ct0502864}, +} + +@article{cummings2021open, + title={Open-source molecular modeling software in chemical engineering focusing on the Molecular Simulation Design Framework}, + author={Cummings, P. T. and McCabe, C. and Iacovella, C. R. and Ledeczi, A. and Jankowski, E. and Jayaraman, A. and et al.}, + journal={AIChE Journal}, + volume={67}, + number={3}, + pages={e17206}, + year={2021}, + doi={https://doi.org/10.1002/aic.17206}, +} + +@article{deng2009computations, + title={Computations of standard binding free energies with molecular dynamics simulations}, + author={Deng, Y. and Roux, B.}, + journal={J Phys Chem B}, + volume={113}, + number={8}, + pages={2234-2246}, + year={2009}, + doi={10.1021/jp807701h}, +} + +@article{gusev2023active, + title={Active Learning Guided Drug Design Lead Optimization Based on Relative Binding Free Energy Modeling}, + author={Gusev, F. and Gutkin, E. and Kurnikova, M. G. and Isayev, O.}, + journal={Journal of Chemical Information and Modeling}, + volume={63}, + number={2}, + pages={583-594}, + year={2023}, + doi={10.1021/acs.jcim.2c01052}, +} + +@article{hedges2023suite, + title={A Suite of Tutorials for the BioSimSpace Framework for Interoperable Biomolecular Simulation [Article v1.0]}, + author={Hedges, L. O. and Bariami, S. and Burman, M. and Clark, F. and Cossins, B. P. and Hardie, A. and et al.}, + journal={Living Journal of Computational Molecular Science}, + volume={5}, + number={1}, + pages={2375}, + year={2023}, + doi={10.33011/livecoms.5.1.2375}, +} + +@article{kirkwood1935statistical, + title={Statistical mechanics of fluid mixtures}, + author={Kirkwood, J. G.}, + journal={The Journal of Chemical Physics}, + volume={3}, + number={5}, + pages={300-313}, + year={1935}, +} + +@article{klimovich2015guidelines, + title={Guidelines for the analysis of free energy calculations}, + author={Klimovich, P. V. and Shirts, M. R. and Mobley, D. L.}, + journal={J Comput Aided Mol Des}, + volume={29}, + number={5}, + pages={397-411}, + year={2015}, + doi={10.1007/s10822-015-9840-9}, +} + +@book{merz2010drug, + title={Drug design: structure-and ligand-based approaches}, + author={Merz Jr, K. M. and Ringe, D. and Reynolds, C. H.}, + year={2010}, + publisher={Cambridge University Press}, +} + +@article{paliwal2011benchmark, + title={A Benchmark Test Set for Alchemical Free Energy Transformations and Its Use to Quantify Error in Common Free Energy Methods}, + author={Paliwal, H. and Shirts, M. R.}, + journal={J Chem Theory Comput}, + volume={7}, + number={12}, + pages={4115-4134}, + year={2011}, + doi={10.1021/ct2003995}, +} + +@article{pham2011identifying, + title={Identifying low variance pathways for free energy calculations of molecular transformations in solution phase}, + author={Pham, T. T. and Shirts, M. R.}, + journal={J Chem Phys}, + volume={135}, + number={3}, + pages={034114}, + year={2011}, + doi={10.1063/1.3607597}, +} + +@article{phillips2020scalable, + title={Scalable molecular dynamics on CPU and GPU architectures with NAMD}, + author={Phillips, J. C. and Hardy, D. J. and Maia, J. D. C. and Stone, J. E. and Ribeiro, J. V. and Bernardi, R. C. and et al.}, + journal={The Journal of Chemical Physics}, + volume={153}, + number={4}, + pages={044130}, + year={2020}, + doi={10.1063/5.0014475}, +} + +@article{pohorille2010good, + title={Good practices in free-energy calculations}, + author={Pohorille, A. and Jarzynski, C. and Chipot, C.}, + journal={J Phys Chem B}, + volume={114}, + number={32}, + pages={10235-10253}, + year={2010}, + doi={10.1021/jp102971x}, +} + +@article{pronk2013gromacs, + title={GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit}, + author={Pronk, S. and Páll, S. and Schulz, R. and Larsson, P. and Bjelkmar, P. and Apostolov, R. and et al.}, + journal={Bioinformatics}, + volume={29}, + number={7}, + pages={845-854}, + year={2013}, + doi={10.1093/bioinformatics/btt055}, diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 2d378935..7c2be3dc 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -22,7 +22,7 @@ affiliations: - name: Institution Name, Country index: 2 -date: 99 August 2023 +date: 29 December 2023 bibliography: paper.bib --- @@ -35,37 +35,37 @@ A distinctive attribute of alchemlyb is its streamlined, end-to-end analysis pro # Statement of need -In the pharmaceutical sector, computational chemistry techniques, particularly relative/absolute binding free energy calculations, are regularly employed to rank potential drug compounds based on their protein-binding affinity. These calculations produce free energy data, which alchemlyb expertly processes to offer free energy estimates. These estimates provide critical insights into the binding affinity at various stages of drug discovery, such as hit identification and lead optimization. alchemlyb's unique capacity to cater to this need has cemented its role as an invaluable asset in computational chemistry. +In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose (Merz Jr, Ringe, & Reynolds, 2010). The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization (Merz Jr et al., 2010). The alchemlyb software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry. -Moreover, within the realm of computational research, different MD engines, including GROMACS, AMBER, OpenMM, and NAMD, have their distinct sets of tools for conducting free energy calculations. This diversity complicates the research process, as data from different engines necessitate unique processing and analysis methods. -The solution to this complication comes in the form of alchemlyb, providing a unified, engine-agnostic analysis workflow. This allows for consistent analysis of free energy data from different MD engines, making it possible for researchers to compare and combine results from various engines in a more streamlined manner. +In the realm of computational research, various molecular dynamics (MD) engines, including GROMACS (Pronk et al., 2013), AMBER (Case et al., 2014), GOMC (Cummings et al., 2021), and NAMD (Phillips et al., 2020), offer distinct tools for conducting free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires unique processing and analysis methods, hindering seamless collaboration and comparison of results. -# Citations -Citations to entries in paper.bib should be in -[rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) -format. +Addressing this complexity is the "alchemical–analysis.py" tool (Klimovich, Shirts, & Mobley, 2015), which precedes alchemlyb. Although "alchemical–analysis.py" has been deprecated, alchemlyb continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, alchemlyb breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -If you want to cite a software repository URL (e.g. something on GitHub without a preferred -citation) then you can do it with the example BibTeX entry below for @fidgit. -For a quick reference, the following citation commands can be used: -- `@author:2001` -> "Author et al. (2001)" -- `[@author:2001]` -> "(Author et al., 2001)" -- `[@author1:2001; @author2:2001]` -> "(Author1 et al., 2001; Author2 et al., 2002)" +Notably, alchemlyb's robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace (Hedges et al., 2023). This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. -# Figures +# Implementation -Figures can be included like this: -![Caption for example figure.\label{fig:example}](figure.png) -and referenced from text using \autoref{fig:example}. +The binding free energy of a drug within a protein is defined as the disparity in free energy between the drug's end-state in the protein's binding pocket and its alternative end-state in a solution, typically water. Absolute binding free energy calculations employ a thermodynamic cycle that establishes a connection between these two end-states through two alchemical legs, namely the bound and free legs (Figure 1). In the bound leg, the drug is decoupled from the binding pocket, while in the free leg, the same drug is decoupled from the solvent. The resulting free energy difference represents the energy required to transfer the drug from the solvent to the protein binding pocket, constituting the binding free energy of the drug. + +To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda that connects the two end-states, leading to the creation of a series of intermediate states. Molecular dynamics (MD) engines are employed to simulate the system at these states, generating and accumulating free energy data. + +Alchemlyb offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states, suitable for free energy perturbation (FEP) methods (Zwanzig, 1954), and dU/dlambda at all lambda states, suitable for thermodynamic integration (TI) methods (Kirkwood, 1935). + +In alchemlyb, TI (Paliwal & Shirts, 2011) and TI with Gaussian quadrature (Gusev, Gutkin, Kurnikova, & Isayev, 2023) methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) (Bennett, 1976) and Multistate BAR (MBAR) (Shirts & Chodera, 2008). These methods necessitate uncorrelated samples, and alchemlyb provides tools for data resampling based on autocorrelation times (Chodera, Swope, Pitera, Seok, & Dill, 2007). + +To evaluate the accuracy of the free energy estimate, alchemlyb offers specific assessment tools. The error of the TI method is correlated with the average curvature (Pham & Shirts, 2011), while the error of perturbation methods depends on the overlap in sampled energy distributions (Pohorille, Jarzynski, & Chipot, 2010). Alchemlyb visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and alchemlyb allows for plotting the convergence of the free energy estimate as a function of simulation time (Yang, Bitetti-Putzer, & Karplus, 2004) to detect potentially un-equilibrated data. + + +Alchemlyb offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, alchemlyb provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as “alchemical–analysis.py" (Klimovich et al., 2015), which is the predecessor of alchemlyb. -Figure sizes can be customized by adding an optional second parameter: -![Caption for example figure.](figure.png){ width=20% } # Acknowledgements We acknowledge contributions from XXXXX during the genesis of this project. -# References \ No newline at end of file +# References + + From 1192a1d25b7f4d15f84ec92292b08cbcd9b05306 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Fri, 29 Dec 2023 20:18:20 +0000 Subject: [PATCH 03/92] fix end --- joss_paper/paper.bib | 1 + 1 file changed, 1 insertion(+) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index f0be9a25..27914214 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -153,3 +153,4 @@ @article{pronk2013gromacs pages={845-854}, year={2013}, doi={10.1093/bioinformatics/btt055}, +} From 0fc12b4a8c85001787ca03d8d338b37ac489231d Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Fri, 29 Dec 2023 20:21:47 +0000 Subject: [PATCH 04/92] update action --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 9612161e..ba0463b1 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -20,4 +20,4 @@ jobs: # This is the output path where Pandoc will write the compiled # PDF. Note, this should be the same directory as the input # paper.md - path: paper.pdf + path: joss_paper/paper.pdf From e8976be6584ef48b2474a20464bb448afeb90862 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Fri, 29 Dec 2023 20:31:23 +0000 Subject: [PATCH 05/92] fix citation --- joss_paper/paper.bib | 33 +++++++++++++++++++++++++++++++++ joss_paper/paper.md | 16 ++++++++-------- 2 files changed, 41 insertions(+), 8 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 27914214..e15f37f0 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -154,3 +154,36 @@ @article{pronk2013gromacs year={2013}, doi={10.1093/bioinformatics/btt055}, } + +@article{shirts2008statistically, + title={Statistically optimal analysis of samples from multiple equilibrium states}, + author={Shirts, M. R. and Chodera, J. D.}, + journal={J Chem Phys}, + volume={129}, + number={12}, + pages={124105}, + year={2008}, + doi={10.1063/1.2978177}, +} + +@article{yang2004free, + title={Free energy simulations: use of reverse cumulative averaging to determine the equilibrated region and the time required for convergence}, + author={Yang, W. and Bitetti-Putzer, R. and Karplus, M.}, + journal={J Chem Phys}, + volume={120}, + number={6}, + pages={2618-2628}, + year={2004}, + doi={10.1063/1.1638996}, +} + +@article{zwanzig1954high, + title={High‐temperature equation of state by a perturbation method. I. Nonpolar gases}, + author={Zwanzig, R. W.}, + journal={The Journal of Chemical Physics}, + volume={22}, + number={8}, + pages={1420-1426}, + year={1954}, +} + diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 7c2be3dc..981d4b16 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -35,16 +35,16 @@ A distinctive attribute of alchemlyb is its streamlined, end-to-end analysis pro # Statement of need -In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose (Merz Jr, Ringe, & Reynolds, 2010). The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization (Merz Jr et al., 2010). The alchemlyb software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry. +In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose [@merz2010drug]. The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. The alchemlyb software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry. -In the realm of computational research, various molecular dynamics (MD) engines, including GROMACS (Pronk et al., 2013), AMBER (Case et al., 2014), GOMC (Cummings et al., 2021), and NAMD (Phillips et al., 2020), offer distinct tools for conducting free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires unique processing and analysis methods, hindering seamless collaboration and comparison of results. +In the realm of computational research, various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for conducting free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires unique processing and analysis methods, hindering seamless collaboration and comparison of results. -Addressing this complexity is the "alchemical–analysis.py" tool (Klimovich, Shirts, & Mobley, 2015), which precedes alchemlyb. Although "alchemical–analysis.py" has been deprecated, alchemlyb continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, alchemlyb breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +Addressing this complexity is the "alchemical–analysis.py" tool [@klimovich2015guidelines], which precedes alchemlyb. Although "alchemical–analysis.py" has been deprecated, alchemlyb continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, alchemlyb breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -Notably, alchemlyb's robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace (Hedges et al., 2023). This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. +Notably, alchemlyb's robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. # Implementation @@ -52,14 +52,14 @@ The binding free energy of a drug within a protein is defined as the disparity i To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda that connects the two end-states, leading to the creation of a series of intermediate states. Molecular dynamics (MD) engines are employed to simulate the system at these states, generating and accumulating free energy data. -Alchemlyb offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states, suitable for free energy perturbation (FEP) methods (Zwanzig, 1954), and dU/dlambda at all lambda states, suitable for thermodynamic integration (TI) methods (Kirkwood, 1935). +Alchemlyb offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states, suitable for free energy perturbation (FEP) methods [@zwanzig1954high], and dU/dlambda at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. -In alchemlyb, TI (Paliwal & Shirts, 2011) and TI with Gaussian quadrature (Gusev, Gutkin, Kurnikova, & Isayev, 2023) methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) (Bennett, 1976) and Multistate BAR (MBAR) (Shirts & Chodera, 2008). These methods necessitate uncorrelated samples, and alchemlyb provides tools for data resampling based on autocorrelation times (Chodera, Swope, Pitera, Seok, & Dill, 2007). +In alchemlyb, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods necessitate uncorrelated samples, and alchemlyb provides tools for data resampling based on autocorrelation times [@chodera2007use]. -To evaluate the accuracy of the free energy estimate, alchemlyb offers specific assessment tools. The error of the TI method is correlated with the average curvature (Pham & Shirts, 2011), while the error of perturbation methods depends on the overlap in sampled energy distributions (Pohorille, Jarzynski, & Chipot, 2010). Alchemlyb visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and alchemlyb allows for plotting the convergence of the free energy estimate as a function of simulation time (Yang, Bitetti-Putzer, & Karplus, 2004) to detect potentially un-equilibrated data. +To evaluate the accuracy of the free energy estimate, alchemlyb offers specific assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. Alchemlyb visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and alchemlyb allows for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. -Alchemlyb offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, alchemlyb provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as “alchemical–analysis.py" (Klimovich et al., 2015), which is the predecessor of alchemlyb. +Alchemlyb offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, alchemlyb provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as “alchemical–analysis.py" [@klimovich2015guidelines], which is the predecessor of alchemlyb. # Acknowledgements From 7816081c9d3a95d0ba8a6a03793594f056b00975 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Fri, 29 Dec 2023 21:59:14 +0000 Subject: [PATCH 06/92] edit --- joss_paper/Fig1.pdf | Bin 0 -> 19638 bytes joss_paper/Fig2.pdf | Bin 0 -> 15761 bytes joss_paper/paper.md | 25 +++++++++++++++---------- 3 files changed, 15 insertions(+), 10 deletions(-) create mode 100644 joss_paper/Fig1.pdf create mode 100644 joss_paper/Fig2.pdf diff --git a/joss_paper/Fig1.pdf b/joss_paper/Fig1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e60938a1f92372c0c85b5887cfdbf155bc907b91 GIT binary patch literal 19638 zcmdUXcRZEv|9?ginHd>zl#zsUj&nFi_8!U19vR2V-m)?xdqhS?Hkn0c$d;LGLN+1U zN%Ff7@Av1EeBQkuk8gkdI1ldobzk@MdR?#UbzRqMT-RYz5R+hsaB$%~NZRYZ10X-iSG!Tp9N+l34ag)7+*4Oo8W7l%{JkI7fw#}vN`R!Qi!yV*`z<%fJ 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The software spans a wide range of functions, starting with the extraction of raw data from Molecular Dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation, using various estimators to derive free energy estimates, and finally providing quality analysis tools for data convergence checking. +*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from Molecular Dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation, using various estimators to derive free energy estimates, and finally providing quality analysis tools for data convergence checking. -A distinctive attribute of alchemlyb is its streamlined, end-to-end analysis process reminiscent of the now-discontinued alchemical analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. +A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis process reminiscent of the now-discontinued [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. # Statement of need -In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose [@merz2010drug]. The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. The alchemlyb software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry. +In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose [@merz2010drug]. The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. The *alchemlyb* software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry [^1]. +[^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). In the realm of computational research, various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for conducting free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires unique processing and analysis methods, hindering seamless collaboration and comparison of results. -Addressing this complexity is the "alchemical–analysis.py" tool [@klimovich2015guidelines], which precedes alchemlyb. Although "alchemical–analysis.py" has been deprecated, alchemlyb continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, alchemlyb breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +Addressing this complexity is the [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which precedes *alchemlyb*. Although [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, *alchemlyb* breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -Notably, alchemlyb's robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. # Implementation The binding free energy of a drug within a protein is defined as the disparity in free energy between the drug's end-state in the protein's binding pocket and its alternative end-state in a solution, typically water. Absolute binding free energy calculations employ a thermodynamic cycle that establishes a connection between these two end-states through two alchemical legs, namely the bound and free legs (Figure 1). In the bound leg, the drug is decoupled from the binding pocket, while in the free leg, the same drug is decoupled from the solvent. The resulting free energy difference represents the energy required to transfer the drug from the solvent to the protein binding pocket, constituting the binding free energy of the drug. -To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda that connects the two end-states, leading to the creation of a series of intermediate states. Molecular dynamics (MD) engines are employed to simulate the system at these states, generating and accumulating free energy data. +![The thermodynamics cycle of Absolute binding free energy calculation](Fig1.pdf) -Alchemlyb offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states, suitable for free energy perturbation (FEP) methods [@zwanzig1954high], and dU/dlambda at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. +To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, leading to the creation of a series of intermediate states. MD engines are employed to simulate the system at these states, generating and accumulating free energy data. -In alchemlyb, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods necessitate uncorrelated samples, and alchemlyb provides tools for data resampling based on autocorrelation times [@chodera2007use]. +*Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states (u_nk), suitable for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. -To evaluate the accuracy of the free energy estimate, alchemlyb offers specific assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. Alchemlyb visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and alchemlyb allows for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods necessitate uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. +To evaluate the accuracy of the free energy estimate, *alchemlyb* offers specific assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and *alchemlyb* allows for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. -Alchemlyb offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, alchemlyb provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as “alchemical–analysis.py" [@klimovich2015guidelines], which is the predecessor of alchemlyb. + +*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, *alchemlyb* provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which is the predecessor of *alchemlyb*. + +![The building blocks of *alchemlyb*](Fig2.pdf) # Acknowledgements From 557d13d25d876c9054785da1e1bd1cbbca0fc524 Mon Sep 17 00:00:00 2001 From: Michael Shirts Date: Sat, 30 Dec 2023 12:05:24 -0700 Subject: [PATCH 07/92] MRS comments (#339) * MRS comments Some notes from MRS * Update joss_paper/paper.md Co-authored-by: Zhiyi Wu --------- Co-authored-by: Zhiyi Wu --- joss_paper/paper.md | 32 +++++++++++++++++--------------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 1401a11d..55042034 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -13,14 +13,19 @@ authors: - name: David Dotson equal-contrib: true # (This is how you can denote equal contributions between multiple authors) affiliation: 2 + - name: Michael R. Shirts + orcid: 0000-0003-3249-1097 + affiliation: 3 - name: Author with no affiliation corresponding: true # (This is how to denote the corresponding author) affiliation: 3 affiliations: - - name: Exscientia, Oxford, UK. + - name: Exscientia, Oxford, UK index: 1 - name: Institution Name, Country index: 2 + - name: University of Colorado Boulder, Boulder, Colorado, USA + index: 3 date: 29 December 2023 bibliography: paper.bib @@ -29,44 +34,41 @@ bibliography: paper.bib # Summary -*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from Molecular Dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation, using various estimators to derive free energy estimates, and finally providing quality analysis tools for data convergence checking. +*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis process reminiscent of the now-discontinued [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. # Statement of need -In the pharmaceutical sector, the utilization of computational chemistry techniques is integral for evaluating potential drug compounds based on their protein-binding affinity [@deng2009computations]. Notably, relative/absolute binding free energy calculations are routinely employed for this purpose [@merz2010drug]. The resultant free energy data is essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. The *alchemlyb* software adeptly processes this data, providing crucial insights and establishing itself as an indispensable asset in computational chemistry [^1]. +In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations [^1]. [^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). -In the realm of computational research, various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for conducting free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires unique processing and analysis methods, hindering seamless collaboration and comparison of results. - +Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for performing free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. -Addressing this complexity is the [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which precedes *alchemlyb*. Although [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, *alchemlyb* breaks down components into individual tools, allowing users to customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +THe [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as Biosimspace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. # Implementation -The binding free energy of a drug within a protein is defined as the disparity in free energy between the drug's end-state in the protein's binding pocket and its alternative end-state in a solution, typically water. Absolute binding free energy calculations employ a thermodynamic cycle that establishes a connection between these two end-states through two alchemical legs, namely the bound and free legs (Figure 1). In the bound leg, the drug is decoupled from the binding pocket, while in the free leg, the same drug is decoupled from the solvent. The resulting free energy difference represents the energy required to transfer the drug from the solvent to the protein binding pocket, constituting the binding free energy of the drug. +The binding free energy of a drug within a protein is defined as the difference in free energy between the drug's end-state in the protein's binding pocket and its alternative end-state in a solution, typically water. Absolute binding free energy calculations employ a thermodynamic cycle that establishes a connection between these two end-states through two alchemical legs, namely the bound and free legs (Figure 1). In the bound leg, the drug is decoupled from the binding pocket, while in the free leg, the same drug is decoupled from the solvent. The resulting free energy difference represents the energy required to transfer the drug from the solvent to the protein binding pocket, constituting the binding free energy of the drug. -![The thermodynamics cycle of Absolute binding free energy calculation](Fig1.pdf) +![The thermodynamics cycle of absolute binding free energy calculation](Fig1.pdf) To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, leading to the creation of a series of intermediate states. MD engines are employed to simulate the system at these states, generating and accumulating free energy data. -*Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between adjacent lambda states (u_nk), suitable for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. +*Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between lambda states (u_nk), which are used for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. -In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods necessitate uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. +In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. -To evaluate the accuracy of the free energy estimate, *alchemlyb* offers specific assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be at an equilibrated state, and *alchemlyb* allows for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. - -*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, *alchemlyb* provides an automated end-to-end tool that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate. This automated workflow allows users to experience a similar process as [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which is the predecessor of *alchemlyb*. +*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate, similar to the precessesor[alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines]. ![The building blocks of *alchemlyb*](Fig2.pdf) - # Acknowledgements We acknowledge contributions from XXXXX during the genesis of this project. From 2057c415e7e1c8944fddc84207d88cd601d775a6 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Sat, 30 Dec 2023 21:16:47 +0000 Subject: [PATCH 08/92] first fix --- joss_paper/paper.md | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 55042034..a304c0a5 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -36,7 +36,7 @@ bibliography: paper.bib *Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. -A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis process reminiscent of the now-discontinued [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. +A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. # Statement of need @@ -53,11 +53,8 @@ Notably, *alchemlyb*'s robust and user-friendly nature has led to its integratio # Implementation -The binding free energy of a drug within a protein is defined as the difference in free energy between the drug's end-state in the protein's binding pocket and its alternative end-state in a solution, typically water. Absolute binding free energy calculations employ a thermodynamic cycle that establishes a connection between these two end-states through two alchemical legs, namely the bound and free legs (Figure 1). In the bound leg, the drug is decoupled from the binding pocket, while in the free leg, the same drug is decoupled from the solvent. The resulting free energy difference represents the energy required to transfer the drug from the solvent to the protein binding pocket, constituting the binding free energy of the drug. +The solvation free energy is a physcial property that is often computed by computational chemist. This quantity can be obtained by constructiong two end states, where one is the ligand in water and interacting with both the water and itself (coupled state) while the other is the ligand interacting with itself but not interacting with the water (decoupled state)and thus, mimicing the pure solvent and ligand in the gas phase. The free energy difference between these two end state would be the solvation free energy. To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, leading to the creation of a series of intermediate states. MD engines are employed to simulate the system at these states, generating and accumulating free energy data. -![The thermodynamics cycle of absolute binding free energy calculation](Fig1.pdf) - -To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, leading to the creation of a series of intermediate states. MD engines are employed to simulate the system at these states, generating and accumulating free energy data. *Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between lambda states (u_nk), which are used for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. @@ -65,9 +62,9 @@ In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gus To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. -*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 2). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate, similar to the precessesor[alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines]. +*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate, which allows one to estimate, for example, the solvation free energy with one line code. The workflow could also be used to faciliate more complicated calculations, such as the Absolute binding free energy, which is just the free energy difference between the solvation free energy of ligand in water and the solvation free energy of the ligand in the binding pocket of the protein. -![The building blocks of *alchemlyb*](Fig2.pdf) +![The building blocks of *alchemlyb*](Fig1.pdf) # Acknowledgements From aa2e189ce6ff22c343fa674321920926097f4329 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Sat, 30 Dec 2023 21:34:24 +0000 Subject: [PATCH 09/92] update the content --- joss_paper/paper.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index a304c0a5..8e11818b 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -36,7 +36,8 @@ bibliography: paper.bib *Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. -A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. +A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation. This attribute enhances accessibility, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. + # Statement of need @@ -53,8 +54,7 @@ Notably, *alchemlyb*'s robust and user-friendly nature has led to its integratio # Implementation -The solvation free energy is a physcial property that is often computed by computational chemist. This quantity can be obtained by constructiong two end states, where one is the ligand in water and interacting with both the water and itself (coupled state) while the other is the ligand interacting with itself but not interacting with the water (decoupled state)and thus, mimicing the pure solvent and ligand in the gas phase. The free energy difference between these two end state would be the solvation free energy. To determine the free energy difference associated with decoupling a drug from its environment, it is essential to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, leading to the creation of a series of intermediate states. MD engines are employed to simulate the system at these states, generating and accumulating free energy data. - +Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: one where the ligand interacts with water and itself (coupled state), and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). The solvation free energy is then obtained by calculating the free energy difference between these two end states. To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. MD engines simulate the system at these states, generating and accumulating free energy data. *Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between lambda states (u_nk), which are used for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. @@ -62,13 +62,13 @@ In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gus To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. -*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs the decorelation, estimation, and quality plotting of the estimate, which allows one to estimate, for example, the solvation free energy with one line code. The workflow could also be used to faciliate more complicated calculations, such as the Absolute binding free energy, which is just the free energy difference between the solvation free energy of ligand in water and the solvation free energy of the ligand in the binding pocket of the protein. +*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. This workflow allows for the estimation of quantities such as solvation free energy with minimal code. Moreover, one could it facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. ![The building blocks of *alchemlyb*](Fig1.pdf) # Acknowledgements -We acknowledge contributions from XXXXX during the genesis of this project. +O.B. and D.D. designed the project. Z.W., D.D., contribute to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W. and M.R.S wrote the manuscript. # References From 87153a0b70b5cd5c6af52370b161661537f60e0f Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Sat, 30 Dec 2023 21:40:56 +0000 Subject: [PATCH 10/92] update --- joss_paper/paper.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 8e11818b..5df0587c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -41,16 +41,16 @@ A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis w # Statement of need -In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations [^1]. - -[^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). +In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations . Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for performing free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. THe [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. + +[^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). # Implementation @@ -68,7 +68,7 @@ To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range # Acknowledgements -O.B. and D.D. designed the project. Z.W., D.D., contribute to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W. and M.R.S wrote the manuscript. +O.B. and D.D. designed the project. Z.W., D.D., contribute to the new features. Z.W., D.D., O.B. maintain the codebase. 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kJtS-=xZyOlRy}H8Dne+fNWG!3M9TI`jX-@Sj=0>FZno5X-L?|GuMB zf*~QM5G3TUfD8ih|8)wkt#t42m@6deY6pQ@!H@tGm^s{5n2rtrHx;JS;!)yMvXg{a zz~$T>VCwG58c=sDsDLS*s0e|OtDvimoeio9fUAwQt)rl;FdYQ-78y(sb$v<((gDyc zNGoAFbXtPWJm^FS001EeQ!_y|DVaYcQFp?07D%L>AQ0%{;=ok@HtZC8ht&YGVWZ!%CE$ zrcgn%)AM_z2*l3L8dXO*wHAm<+=PIC|AZQr(?6%o$Wyq9pa)6Z8u|0W8h&b}AS(4U z7p8M%H-(u&oUD;_B4?68A>io{Si?nrUZ90foA{~lmlmL>c_zZ)r)mj^`G0QX&m8eL z^=P?M^?z#nr2*5wr{({%BY#8Ww`Tt8#jiU5YzhTIL3An$JIzx?P7`K!5SSe-paBL6 z@^A|BfF5W_3ITs(`&Am1;=;|`{;TvaY-gnZSYrQO{#V9dG$<(vN+F<5r)ZRy5^-{Z zn+lrpLHM|#X58#%5SRcvHv|f17chgEutQ;H{QRaMGd?a;ZgiK<%AQp!Z|jJJ*g|3d z7p1?*qLu!fkY83BdJUtohFZiZR{;FW7ocwbawVwe5QOPmPJP8co=pJp{&Ir!r<+1K zjWC~+!59IWJR%@C+ljMGb)DFEr9t;?weNt zc-pzYdak91+B^fm2LI1(leQYn>~FX2n#Mk)r2ZB{fjgH+M=uc|iC7w52@K=MWs(Z1 zzn(=*rWz3#C>>7ji4p#o+^pO>hEmZi2@W5?Ei1Ez3=B3)oP5zwZ*zP*b+DK|+Y1a> zC@v;r@P^^xvv1Ok^u+X2%a4!je!xDFv6L;tX{p7UnY*TWd#^a@wUC$0APb?%W!tX! zH}c6k9&H(Nk~d3Vgc!xNv$b#C6r~%#LpaCYD4U`%;7{q%zA5L(WC+1z-MJ^JU}Ge~ ztZee+$~#c#j}|)8Y@D^So6p94BF6&2)-nQ>#;kYBh$dfPCya#}Eu@BXutA^w743jD@Y3-%dn_=NR79q>UGflj2@nwbPcWkvngh}m1uOy(515mS LfPq2!zRdps@dSH% From 081e90c834b5a1794e2a5b95801639e7c91e7e02 Mon Sep 17 00:00:00 2001 From: David Dotson Date: Mon, 8 Apr 2024 19:52:02 -0700 Subject: [PATCH 12/92] Small edits, clauses set as newlines for easier diffs --- joss_paper/paper.md | 77 +++++++++++++++++++++++++++++++++++---------- 1 file changed, 61 insertions(+), 16 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 5df0587c..c5e3fbfb 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -22,7 +22,7 @@ authors: affiliations: - name: Exscientia, Oxford, UK index: 1 - - name: Institution Name, Country + - name: Datryllic LLC, Phoenix, AZ, USA index: 2 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 3 @@ -34,41 +34,86 @@ bibliography: paper.bib # Summary -*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. +*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, + an integral part of computational chemistry and biology, + most notably in the field of drug discovery. +The software spans a wide range of functions, + starting with the extraction of raw data from molecular dynamics (MD) engines, + moving on to data preprocessing tasks such as decorrelation of time series, + using various estimators to derive free energy estimates from simulation samples, + and finally providing quality analysis tools for data convergence checking. -A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation. This attribute enhances accessibility, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize alchemlyb effectively. +A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. +This user-friendly workflow facilitates navigation through the entire analysis pipeline, + from the initial data input stage to the final result derivation. This attribute enhances accessibility, + enabling researchers from diverse scientific backgrounds, + and not solely computational chemistry specialists, + to utilize *alchemlyb* effectively. # Statement of need -In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations . +In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. +Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. +The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. +Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. +The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations. -Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for performing free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. +Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], + offer distinct tools for performing free energy calculations. +However, the diversity in output formats and analysis tools among different MD engines complicates the research process. +Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. -THe [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +THe [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. +Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, + *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. +Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, + allowing users to more easily customize their analysis. +This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. -Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. +This further enhances its accessibility and usability within broader scientific workflows, + reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. [^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). # Implementation -Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: one where the ligand interacts with water and itself (coupled state), and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). The solvation free energy is then obtained by calculating the free energy difference between these two end states. To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter lambda ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. MD engines simulate the system at these states, generating and accumulating free energy data. +Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: + one where the ligand interacts with water and itself (coupled state), + and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). +The solvation free energy is then obtained by calculating the free energy difference between these two end states. +To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. +Overlapping is facilitated by introducing a parameter `lambda` ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. +MD engines simulate the system at these states, generating and accumulating free energy data. + +*alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard `pandas` `DataFrames`. +Two types of free energy data are considered: reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high], + and $dU/d\lambda$ at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical]. + +In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators. +FEP category estimators include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. +These estimators assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. + +To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. +The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. +*alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. +Additionally, the accumulated samples should be collected from equilibrated simulations, + and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. + +*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). +Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. +This workflow allows for the estimation of quantities such as solvation free energy with minimal code. +Moreover, this facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. -*Alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard pandas dataframes. Two types of free energy data are considered: potential energy differences between lambda states (u_nk), which are used for free energy perturbation (FEP) methods [@zwanzig1954high], and $dU/d\lambda $ at all lambda states, suitable for thermodynamic integration (TI) methods [@kirkwood1935statistical]. - -In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] methods are implemented in the TI category. Perturbation category methods include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These methods assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. - -To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of perturbation methods depends on the overlap in sampled energy distributions [@pohorille2010good]. *Alchemlyb* visualizes the smoothness of the integrand for TI methods and the overlap matrix for perturbation methods. Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +![The building blocks of *alchemlyb*](Fig1.pdf) -*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. This workflow allows for the estimation of quantities such as solvation free energy with minimal code. Moreover, one could it facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. -![The building blocks of *alchemlyb*](Fig1.pdf) # Acknowledgements -O.B. and D.D. designed the project. Z.W., D.D., contribute to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S wrote the manuscript. +O.B. and D.D. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S wrote the manuscript. # References From b5c3d1d528088db3c941b2b287957359ef66e9a0 Mon Sep 17 00:00:00 2001 From: David Dotson Date: Mon, 8 Apr 2024 20:41:11 -0700 Subject: [PATCH 13/92] Some additional minor edits for internal consistency around estimators, standard data forms --- joss_paper/paper.md | 34 ++++++++++++++++++++-------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index c5e3fbfb..e87ed352 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -10,8 +10,9 @@ authors: orcid: 0000-0002-7615-7851 equal-contrib: true affiliation: 1 - - name: David Dotson + - name: David L. Dotson equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + orcid: 0000-0001-5879-2942 affiliation: 2 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 @@ -45,10 +46,11 @@ The software spans a wide range of functions, A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, - from the initial data input stage to the final result derivation. This attribute enhances accessibility, - enabling researchers from diverse scientific backgrounds, - and not solely computational chemistry specialists, - to utilize *alchemlyb* effectively. + from the initial data input stage to the final result derivation. +This attribute enhances accessibility, + enabling researchers from diverse scientific backgrounds, + and not solely computational chemistry specialists, + to utilize *alchemlyb* effectively. # Statement of need @@ -60,21 +62,23 @@ Other free energies extracted from simulations are useful in solution thermodyna The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations. Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], - offer distinct tools for performing free energy calculations. + offer distinct tools for performing free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. -Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. +Data generated by each engine requires individualized processing and analysis methods, + hindering seamless collaboration and comparison of results. -THe [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. +The [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. -Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, +Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. -This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +This innovation enables consistent processing of free energy data from diverse MD engines, + facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, - reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. + reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. [^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). @@ -89,15 +93,17 @@ Overlapping is facilitated by introducing a parameter `lambda` ($\lambda $) that MD engines simulate the system at these states, generating and accumulating free energy data. *alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard `pandas` `DataFrames`. -Two types of free energy data are considered: reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high], - and $dU/d\lambda$ at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical]. +Two types of free energy data are considered: + Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], + and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high], In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators. FEP category estimators include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These estimators assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. -The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. +The error of the TI method is correlated with the average curvature [@pham2011identifying], + while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. *alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. From ed01447f6c9b1edd2b2add98ced112063efe157c Mon Sep 17 00:00:00 2001 From: David Dotson Date: Mon, 8 Apr 2024 20:44:54 -0700 Subject: [PATCH 14/92] Small rearrangement of metadata items --- joss_paper/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index e87ed352..60894adb 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -11,13 +11,14 @@ authors: equal-contrib: true affiliation: 1 - name: David L. Dotson - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) orcid: 0000-0001-5879-2942 + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) affiliation: 2 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 affiliation: 3 - name: Author with no affiliation + orcid: corresponding: true # (This is how to denote the corresponding author) affiliation: 3 affiliations: From 0559c9ffe375c8ef42bfcc466336bf538447737a Mon Sep 17 00:00:00 2001 From: David Dotson Date: Mon, 8 Apr 2024 20:46:09 -0700 Subject: [PATCH 15/92] Set alchemlyb to lowercase throughout (stylistic choice); small rearrangement of paper metadata items --- joss_paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 60894adb..fb95397b 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -1,5 +1,5 @@ --- -title: 'Alchemlyb: The Simple Alchemistry Library' +title: 'alchemlyb: the simple alchemistry library' tags: - Python - alchemistry @@ -36,7 +36,7 @@ bibliography: paper.bib # Summary -*Alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, +*alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. The software spans a wide range of functions, @@ -109,7 +109,7 @@ The error of the TI method is correlated with the average curvature [@pham2011id Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. -*Alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). +*alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. This workflow allows for the estimation of quantities such as solvation free energy with minimal code. Moreover, this facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. From 13b13c76653d8f92c1ca71714ac383f13cb8c759 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 24 Apr 2024 17:56:28 -0700 Subject: [PATCH 16/92] added Oliver as author --- joss_paper/paper.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index fb95397b..d221147c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -17,10 +17,10 @@ authors: - name: Michael R. Shirts orcid: 0000-0003-3249-1097 affiliation: 3 - - name: Author with no affiliation - orcid: + - name: Oliver Beckstein + orcid: 0000-0003-1340-0831 corresponding: true # (This is how to denote the corresponding author) - affiliation: 3 + affiliation: 4 affiliations: - name: Exscientia, Oxford, UK index: 1 @@ -28,8 +28,10 @@ affiliations: index: 2 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 3 + - name: Arizona State University, Tempe, Arizona, USA + index: 4 -date: 29 December 2023 +date: 24 April 2024 bibliography: paper.bib --- From 850ec5afc1b1b0d6fb3887c8b4f47ec83d436b2f Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 24 Apr 2024 18:11:05 -0700 Subject: [PATCH 17/92] consistent line breaking in markdown - infinite linesbreak at periods - hard line break at periods so that diffs are limited to sentences No text changes. --- joss_paper/paper.md | 52 ++++++++++++--------------------------------- 1 file changed, 14 insertions(+), 38 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index d221147c..6d3d254a 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -38,22 +38,10 @@ bibliography: paper.bib # Summary -*alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, - an integral part of computational chemistry and biology, - most notably in the field of drug discovery. -The software spans a wide range of functions, - starting with the extraction of raw data from molecular dynamics (MD) engines, - moving on to data preprocessing tasks such as decorrelation of time series, - using various estimators to derive free energy estimates from simulation samples, - and finally providing quality analysis tools for data convergence checking. - -A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. -This user-friendly workflow facilitates navigation through the entire analysis pipeline, - from the initial data input stage to the final result derivation. -This attribute enhances accessibility, - enabling researchers from diverse scientific backgrounds, - and not solely computational chemistry specialists, - to utilize *alchemlyb* effectively. +*alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. +The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. + +A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation. This attribute enhances accessibility, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize *alchemlyb* effectively. # Statement of need @@ -64,52 +52,40 @@ The resultant estimates of these free energies are essential for understanding b Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations. -Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], - offer distinct tools for performing free energy calculations. +Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for performing free energy calculations. However, the diversity in output formats and analysis tools among different MD engines complicates the research process. -Data generated by each engine requires individualized processing and analysis methods, - hindering seamless collaboration and comparison of results. +Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. The [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. -Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, - *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. -Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, - allowing users to more easily customize their analysis. -This innovation enables consistent processing of free energy data from diverse MD engines, - facilitating streamlined comparison and combination of results. +Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. +Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. +This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. -This further enhances its accessibility and usability within broader scientific workflows, - reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. +This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. [^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). # Implementation -Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: - one where the ligand interacts with water and itself (coupled state), - and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). +Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: one where the ligand interacts with water and itself (coupled state), and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). The solvation free energy is then obtained by calculating the free energy difference between these two end states. To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Overlapping is facilitated by introducing a parameter `lambda` ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. MD engines simulate the system at these states, generating and accumulating free energy data. *alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard `pandas` `DataFrames`. -Two types of free energy data are considered: - Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], - and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high], +Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators. FEP category estimators include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. These estimators assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. -The error of the TI method is correlated with the average curvature [@pham2011identifying], - while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. +The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. *alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. -Additionally, the accumulated samples should be collected from equilibrated simulations, - and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. From 6e7fdb6e175f8b4926b313c17e705bff102008fd Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 24 Apr 2024 18:13:00 -0700 Subject: [PATCH 18/92] updated Acknowledgements/Contributions - move DD up in design - added OB to writing --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 6d3d254a..fe50edbc 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -98,7 +98,7 @@ Moreover, this facilitates more complex calculations, such as absolute binding f # Acknowledgements -O.B. and D.D. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S wrote the manuscript. +D.D. and O.B. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S, O.B. wrote the manuscript. # References From 1c2ec9da1bf347837dedf556ce67945f23b1deb4 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 24 Apr 2024 18:19:21 -0700 Subject: [PATCH 19/92] edited summary --- joss_paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index fe50edbc..c9e1df3c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -38,10 +38,10 @@ bibliography: paper.bib # Summary -*alchemlyb* is a dedicated open-source software package tailored for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. -The software spans a wide range of functions, starting with the extraction of raw data from molecular dynamics (MD) engines, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking. +*alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. +Its functionality covers contains individual function-based building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. -A distinctive attribute of *alchemlyb* is its streamlined, end-to-end analysis workflow. This user-friendly workflow facilitates navigation through the entire analysis pipeline, from the initial data input stage to the final result derivation. This attribute enhances accessibility, enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize *alchemlyb* effectively. +*alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final result derivation. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize *alchemlyb* effectively. # Statement of need From 0b14e7c6dee86f4ccfc6d64220a6876b920000ff Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 24 Apr 2024 18:41:22 -0700 Subject: [PATCH 20/92] WIP: editing statement of need --- joss_paper/paper.md | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index c9e1df3c..c2e1e86b 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -39,7 +39,7 @@ bibliography: paper.bib # Summary *alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. -Its functionality covers contains individual function-based building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. +Its functionality covers contains individual function-based building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. *alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final result derivation. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize *alchemlyb* effectively. @@ -50,11 +50,17 @@ In the pharmaceutical sector, computational chemistry techniques are integral fo Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. -The *alchemlyb* software processes the raw data from MD simulations using key estimators from statistical mechanics, drastically simplifying the process of extracting crucial thermodynamic insights from molecular simulations. -Various molecular dynamics (MD) engines, including GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], GOMC [@cummings2021open], and NAMD [@phillips2020scalable], offer distinct tools for performing free energy calculations. -However, the diversity in output formats and analysis tools among different MD engines complicates the research process. -Data generated by each engine requires individualized processing and analysis methods, hindering seamless collaboration and comparison of results. +Molecular dynamics (MD) packages such as GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. +However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. +Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. +Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines] or are out of date +Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across. + +*alchemlyb* addresses this problem by focusing only on the data analysis with the goal to provide a unified interface for working with free energy data. +In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, a pandas Dataframe. +Additional functions enable subsampling or decorrelation of data and applying estimators from statistical mechanics to derive free energy quantities. +Overall, *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. The [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. From 6b202b2c8c9272dd7bb47aa229ac28d9bf8be6ed Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 25 Apr 2024 10:17:04 -0700 Subject: [PATCH 21/92] removed download stats Manual condastats were about 30k, condastats gace 5k, pypistats 25k for 2023-10-27 - 2024-04-24, so I am not sure how best to measure and in any case, these numbers will not age well in a paper so I am going to remove them. We can write in our cover letter that we have sizable downloads. --- joss_paper/paper.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index c2e1e86b..db18082c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -69,9 +69,8 @@ Unlike its predecessor, *alchemlyb* breaks down components of the workflow into This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. -This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry [^1]. +This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. -[^1]: As of 29/12/2023, *alchemlyb* has been downloaded 23,922 times from [conda-forge](https://anaconda.org/conda-forge/alchemlyb/files). # Implementation From 5002923525652cc40d111937260f033af84a0a6d Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 25 Apr 2024 10:26:40 -0700 Subject: [PATCH 22/92] completed editing of Statement of need section --- joss_paper/paper.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index db18082c..cb25f41f 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -62,11 +62,11 @@ In an initial step data are read from the native MD package file formats and the Additional functions enable subsampling or decorrelation of data and applying estimators from statistical mechanics to derive free energy quantities. Overall, *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. - -The [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which preceeded *alchemlyb*, addressed this problem. -Now that [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) has been deprecated, *alchemlyb* continues to provide a unified, engine-agnostic analysis workflow. -Unlike its predecessor, *alchemlyb* breaks down components of the workflow into modular tools, allowing users to more easily customize their analysis. -This innovation enables consistent processing of free energy data from diverse MD engines, facilitating streamlined comparison and combination of results. +*alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. +`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design. +*alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. +Thus, *alchemlyb* is primarily a library that enables users to easily use well-tested building blocks with in their own tools while additionally providing examples of complete end-to-end workflows. +This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. From 0ca06f6df2e237f4c07af6a14f9e31c4f316f2de Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 25 Apr 2024 11:00:08 -0700 Subject: [PATCH 23/92] Ack/Author contrib update - create Author Contributions section - add funding to Ack section --- joss_paper/paper.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index cb25f41f..b6aafebe 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -103,8 +103,13 @@ Moreover, this facilitates more complex calculations, such as absolute binding f # Acknowledgements +Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). + +# Author contributions + D.D. and O.B. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S, O.B. wrote the manuscript. + # References From e8d57e957d9170b076abca2f2393b2eb989f449a Mon Sep 17 00:00:00 2001 From: Michael Shirts Date: Sat, 27 Apr 2024 11:49:02 -0600 Subject: [PATCH 24/92] Update paper.md with some comments. Some additional rephrasings and clarifications. Also adding another reference in (will do in another commit) --- joss_paper/paper.md | 30 ++++++++++++++---------------- 1 file changed, 14 insertions(+), 16 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index b6aafebe..e848bcee 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -54,18 +54,18 @@ Other free energies extracted from simulations are useful in solution thermodyna Molecular dynamics (MD) packages such as GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. -Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines] or are out of date -Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across. +Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines,@mey2020bestpractices] or are out of date. +Overall, the lack of coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. -*alchemlyb* addresses this problem by focusing only on the data analysis with the goal to provide a unified interface for working with free energy data. -In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, a pandas Dataframe. -Additional functions enable subsampling or decorrelation of data and applying estimators from statistical mechanics to derive free energy quantities. -Overall, *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. +*alchemlyb* addresses this problem by focusing only the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different MD packages. +In an initial step, data are read from the native MD package file formats and then organized into a common standard data structure, organized as a pandas Dataframe. +Additional functions enable subsampling or decorrelation of data and applying statistical mechanical estimators to extract the free energies and thermodynamic expectations as well associated metrics of quality. +*alchemlyb* implements these workflows using modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. *alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. -`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design. +`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, as well as remaining in python 2. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. -Thus, *alchemlyb* is primarily a library that enables users to easily use well-tested building blocks with in their own tools while additionally providing examples of complete end-to-end workflows. +Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks with in their own tools while additionally providing examples of complete end-to-end workflows. This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. @@ -74,33 +74,31 @@ This further enhances its accessibility and usability within broader scientific # Implementation -Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: one where the ligand interacts with water and itself (coupled state), and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). +Transfer free energies, key physical property often computed by computational chemists, involves constructing two end states where a target molecule interacts with different environments For example, in a solvation free energy calculation, at one state (the coupled state) it interacts with a solvent (in the case hydration free energies, water), and the other where the ligand has no intermolecular interactions (the decoupled state), mimicking the transfer of a ligand at infinite dilution in the solvent at one end of the process and then ligand in the gas phase at the other. The solvation free energy is then obtained by calculating the free energy difference between these two end states. To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. -Overlapping is facilitated by introducing a parameter `lambda` ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. -MD engines simulate the system at these states, generating and accumulating free energy data. +The creation of overlapping states is facilitated by introducing a parameter `lambda` ($\lambda$) that continuously connects the functional form of the two end-states, resulting in a series of intermediate states each with a $\lambda$ value ranging from 0 to 1 (inclusive) are simulated. MD engines simulate the system at these states at these intermediate alchemical states, generating and accumulating free energy data discussed below. *alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard `pandas` `DataFrames`. Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators. FEP category estimators include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. -These estimators assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. +These estimators assume uncorrelated samples in order to give unbiased estimates of the uncertainties, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. -*alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. -Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +*alchemlyb* creates visualizations of the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators, which can be qualitatively and quantitatively analyzed to determine the degree of overlap between simulated alchemical states, and suggest whether additional simulations should be run. +For statistical validity, the accumulated samples should be collected from equilibrated simulations, an *alchemlyb* thus also has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect the presence of potentially un-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). -Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. +Additionally, *alchemlyb* provides an automated end-to-end workflow that carries out all stages of the analysing, reading in the raw input data and performs decorrelation, estimation, and quality plotting of the estimates. This workflow allows for the estimation of quantities such as solvation free energy with minimal code. Moreover, this facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. ![The building blocks of *alchemlyb*](Fig1.pdf) - # Acknowledgements Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). From eca0066b84e560a979c63c3aaecbbda4f02db62f Mon Sep 17 00:00:00 2001 From: Michael Shirts Date: Sat, 27 Apr 2024 11:51:46 -0600 Subject: [PATCH 25/92] Update paper.bib added Mey et al. on best practices. --- joss_paper/paper.bib | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index e15f37f0..2cce4b6f 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -186,4 +186,12 @@ @article{zwanzig1954high pages={1420-1426}, year={1954}, } - +@article{mey2020bestpractices, +title = {Best Practices for Alchemical Free Energy Calculations [Article v1.0}, + author = {Mey, Antonia S. J. S. and Allen, Bryce K. and McDonald, Hannah E. Bruce and Chodera, John D. and Hahn, David F. and Kuhn, Maxmillian and Michel, Julien and Mobley, David L. and Naden, Levi N. and Prasad, Samarjeet and Rizzi, Andrea and Scheen, Jenke and Shirts, Michael R. and Tresadern, Gary and Xu, Huafeng}, + year = {2020}, + journal = {Living J. Comput. Mol. Sci.}, + volume = {2}, + number = {1}, + pages = {18378}, +} From ae955d82a7fc8c09272403c155b624b41d7420c6 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 14:09:35 -0700 Subject: [PATCH 26/92] add MDPOW --- joss_paper/paper.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index b6aafebe..2f4040bf 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -31,7 +31,7 @@ affiliations: - name: Arizona State University, Tempe, Arizona, USA index: 4 -date: 24 April 2024 +date: 31 May 2024 bibliography: paper.bib --- @@ -65,11 +65,10 @@ Overall, *alchemlyb* implements modular building blocks to simplify the process *alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. `alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. -Thus, *alchemlyb* is primarily a library that enables users to easily use well-tested building blocks with in their own tools while additionally providing examples of complete end-to-end workflows. +Thus, *alchemlyb* is primarily a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. -Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite]. -This further enhances its accessibility and usability within broader scientific workflows, reinforcing its position as a versatile and essential tool in the field of computational chemistry. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite] or MDPOW [@fan2020aa], demonstrating its accessibility and usability within broader scientific workflows and reinforcing its position as a versatile tool in the field of computational chemistry. # Implementation From e08a52e7bdd5696383ed5beda024c35a8b7f4961 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 14:16:34 -0700 Subject: [PATCH 27/92] add fractional equilibration time --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 2f4040bf..f6004b08 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -89,7 +89,7 @@ These estimators assume uncorrelated samples, and *alchemlyb* provides tools for To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. *alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. -Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] to detect potentially un-equilibrated data. +Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. From 64de66d6ddd8c7bee2d0248fcfceeaffd8b5a4e7 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 16:46:39 -0700 Subject: [PATCH 28/92] edited Implementation section - major rewrite: added sub headings - added more citations (scikit-learn, pandas, MDPOW) as well as Mey 2020 - added design principles from https://alchemlyb.readthedocs.io/en/latest/api_principles.html - added full caption for figure - added license (BSD-3) --- joss_paper/paper.bib | 67 +++++++++++++++++++++++++++++++++++++++++ joss_paper/paper.md | 71 ++++++++++++++++++++++++++++++++------------ 2 files changed, 119 insertions(+), 19 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index e15f37f0..11b52da9 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -187,3 +187,70 @@ @article{zwanzig1954high year={1954}, } + +@article{fan2020aa, + author = {Fan, Shujie and Iorga, Bogdan I. and Beckstein, Oliver}, + journal = {Journal of Computer-Aided Molecular Design}, + pages = {543--560}, + title = {Prediction of octanol-water partition coefficients for the {SAMPL6}-{$\log P$} molecules using molecular dynamics simulations with {OPLS-AA}, {AMBER} and {CHARMM} force fields}, + volume = {34}, + year = {2020} +} + + +@article{Mey2020aa, + author = {Antonia S. J. S. Mey and Bryce Allen and Hannah E. Bruce Macdonald and John D. Chodera and Maximilian Kuhn and Julien Michel and David L. Mobley and Levi N. Naden and Samarjeet Prasad and Andrea Rizzi and Jenke Scheen and Michael R. Shirts and Gary Tresadern and Huafeng Xu}, + journal = {Living Journal of Computational Molecular Science}, + number = {1}, + pages = {18378}, + title = {Best Practices for Alchemical Free Energy Calculations}, + volume = {2}, + year = {2020} +} + + +@article{Chodera2016aa, + author = {Chodera, John D.}, + journal = {Journal of Chemical Theory and Computation}, + month = {Apr}, + number = {4}, + pages = {1799--1805}, + title = {A Simple Method for Automated Equilibration Detection in Molecular Simulations}, + volume = {12}, + year = {2016}} + +@InProceedings{ mckinney-proc-scipy-2010, + author = { {W}es {M}c{K}inney }, + title = { {D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython }, + booktitle = { {P}roceedings of the 9th {P}ython in {S}cience {C}onference }, + pages = { 56 - 61 }, + year = { 2010 }, + editor = { {S}t\'efan van der {W}alt and {J}arrod {M}illman }, + doi = { 10.25080/Majora-92bf1922-00a } +} + + +@article{scikitlearn2011, + title={Scikit-learn: Machine Learning in {P}ython}, + author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. + and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, + journal={Journal of Machine Learning Research}, + volume={12}, + pages={2825--2830}, + year={2011} +} + +@inproceedings{sklearn2013api, + author = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and + Fabian Pedregosa and Andreas Mueller and Olivier Grisel and + Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort + and Jaques Grobler and Robert Layton and Jake VanderPlas and + Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux}, + title = {{API} design for machine learning software: experiences from the scikit-learn + project}, + booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning}, + year = {2013}, + pages = {108--122}, +} \ No newline at end of file diff --git a/joss_paper/paper.md b/joss_paper/paper.md index f6004b08..8771710e 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -54,12 +54,13 @@ Other free energies extracted from simulations are useful in solution thermodyna Molecular dynamics (MD) packages such as GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. -Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines] or are out of date -Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across. +Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines,@Mey2020aa] or are out of date +Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across packages. *alchemlyb* addresses this problem by focusing only on the data analysis with the goal to provide a unified interface for working with free energy data. -In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, a pandas Dataframe. -Additional functions enable subsampling or decorrelation of data and applying estimators from statistical mechanics to derive free energy quantities. +In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, a *pandas* `Dataframe` [@mckinney-proc-scipy-2010] (https://pandas.pydata.org). +Functions are provided for pre-processing data by subsampling or decorrelation. +Statistical mechanical estimators are available to derive free energy quantities; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011,@sklearn2013api] (https://scikit-learn.org). Overall, *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. *alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. @@ -73,30 +74,62 @@ Notably, *alchemlyb*'s robust and user-friendly nature has led to its integratio # Implementation -Solvation free energy, a key physical property often computed by computational chemists, involves constructing two end states: one where the ligand interacts with water and itself (coupled state), and the other where the ligand interacts only with itself, mimicking the pure solvent and ligand in the gas phase (decoupled state). -The solvation free energy is then obtained by calculating the free energy difference between these two end states. -To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. -Overlapping is facilitated by introducing a parameter `lambda` ($\lambda $) that connects the two end-states, resulting in a series of intermediate states. -MD engines simulate the system at these states, generating and accumulating free energy data. +Free energy differences are fundamental to understand many different processes at the molecular scale, ranging from the binding of drug molecules to their receptor proteins or nucleic acids through the partitioning of molecules into different solvents or phases to the stability of crystals and biomolecules. +The calculation of free energy differences is challenging but in the specific case of binding free energies and partitioning free energies, alchemical free energy calculations have emerged as a de-facto standard approach whereby non-physical intermediate states are introduced to bridge between the physical end states of the process of interest, such as the drug bound to the receptor and the drug free in solution [@Mey2020aa]. +In stratified ("windowed") alchemical free energy calculations, the system's interaction (its Hamiltonian) is continuously transformed as a function of a vector of $N$ alchemical parameters $\vec{\lambda}=(\lambda_1, \lambda_2, \dots, \lambda_N)$, so that in general $\vec{\lambda}=(0, 0, \dots, 0)$ indicates the initial physically realizable state and $\vec{\lambda} = (1, 1, \dots, 1)$ the final physically realizable state, while any intermediate configurations are non-physical but required for converging the calculations. +At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using molecular dynamics (MD) or Monte Carlo (MC) simulations and relevant quantities are calculated and stored for each sampled conformation. +Estimators are then applied to these quantities to yield free energy differences between states and thus between the final and initial state, which equals the desired physical free energy difference. -*alchemlyb* offers specific parsers designed to load raw free energy data from various MD engines, converting them into standard `pandas` `DataFrames`. +## Core design principles + +*alchemlyb* is a Python library that seeks to make doing alchemical free energy calculations easier and less error prone. +It includes functionality for parsing data from file formats of widely used simulation packages, subsampling these data, and fitting these data with an estimator to obtain free energies. +Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as a classes that follow the tried-and-tested scikit-learn API. +General and robust workflows following best practices are also provided, which can be used as reference implementations and examples. + +First and foremost, scientific code must be correct and we try to ensure this requirement by following best software engineering practices during development, close to full test coverage of all code in the library (currently 99%), and providing citations to published papers for included algorithms. +We use a curated, public data set (*alchemtest* (https://github.com/alchemistry/alchemtest)) for automated testing. + +The guiding design principles are summarized as: + +1. Use functions when possible, classes only when necessary (or for estimators, see (2)). +2. For estimators, mimic the object-oriented scikit-learn API as much as possible. +3. Aim for a consistent interface throughout, e.g. all parsers take similar inputs and yield a common set of outputs, using the `pandas.DataFrame` as the underlying data structure. +4. Have *all* functionality tested. + +*alchemlyb* is published under the open source BSD-3 clause license. + +## Library structure + +*alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various MD packages (GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open]). +The raw data are converted into a standard format as a `pandas.DataFrame` and converted from the energy of the software to units of $k T$ where $k = 1.380649 \times 10^{-23}\,\text{J}\,\text{K}^{-1}$ is Boltzmann's constant and $T$ is the temperature at which the simulation was performed. +Metadata such as $T$ and the energy unit are stored in DataFrame attributes and propagated through *alchemlyb*, which enables seamless unit conversion with functions in the `alchemlyb.postprocessing` module. Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. -In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators. -FEP category estimators include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically]. -These estimators assume uncorrelated samples, and *alchemlyb* provides tools for data resampling based on autocorrelation times [@chodera2007use]. +Both types of estimators assume uncorrelated samples, which requires subsampling of the raw data. +The `alchemlyb.preprocessing.subsampling` module provides tools for data subsampling based on autocorrelation times [@chodera2007use,@Chodera2016aa] as well as simple slicing of the `dHdl` and `u_nk` DataFrames. + +The two major classes of commonly used estimators are implemented in `alchemlyb.estimators`. +Unlike other components of *alchemlyb* that are implemented as pure functions, estimators are implemented as classes and follow the well-known scikit-learn API [@sklearn2013api] where instantiation sets the parameters (e.g., `estimator = MBAR(maximum_iterations=10000)`) and calling of the `fit()` method (e.g., `estimator.fit(u_nk)`) applies the estimator to the data and populates output attributes of the class; these results attributes are customarily indicated with a trailing underscore (e.g., `estimator.delta_f_` for the matrix of free energy differences between all states). +In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators (module `alchemlyb.estimators.TI`). +FEP category estimators (module `alchemlyb.estimators.FEP`) include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically], which are implemented in the `pymbar` package [@shirts2008statistically] (https://github.com/choderalab/pymbar) and called from *alchemlyb*. To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. -*alchemlyb* visualizes the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. -Additionally, the accumulated samples should be collected from equilibrated simulations, and *alchemlyb* has tools for plotting the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. +*alchemlyb* the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators. +The accumulated samples should be collected from equilibrated simulations and *alchemlyb* contains tools for assessing (`alchemlyb.convergence`) and plotting (`alchemlyb.visualisation`) the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). -Additionally, *alchemlyb* provides an automated end-to-end workflow that reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. -This workflow allows for the estimation of quantities such as solvation free energy with minimal code. -Moreover, this facilitates more complex calculations, such as absolute binding free energy, which is the free energy difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket. -![The building blocks of *alchemlyb*](Fig1.pdf) +![The building blocks of *alchemlyb*. Raw data from simulation packages are parsed into common data structures depending on the free energy quantities, pre-processed, and processed with a free energy estimator. The resulting free energy differences are analyzed for convergence and plotted for quality assessment.](Fig1.pdf) + + +## Workflows + +The building blocks are sufficient to compute free energies from alchemical free energy simulations and assess their reliability. +*alchemlyb* also provides a structure to combined the building blocks into full end-to-end workflows (module `alchemlyb.workflows`). +As an example, the `ABFE` workflow for absolute binding free energy estimation reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. +It can directly estimate quantities such as solvation free energies and makes it easy to calculate more complex quantities such as absolute binding free energies (as the difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket). From cb5e5dc3cee9e955763d7fee5867e06f05a09295 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 17:30:51 -0700 Subject: [PATCH 29/92] spell checked --- joss_paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index a090ffdf..2d11ab92 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -58,7 +58,7 @@ Furthermore, the native analysis tools do not always implement current best prac Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. *alchemlyb* addresses this problem by focusing only on the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different MD packages. -In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a *pandas* `Dataframe` [@mckinney-proc-scipy-2010] (https://pandas.pydata.org). +In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a *pandas* `DataFrame` [@mckinney-proc-scipy-2010] (https://pandas.pydata.org). Functions are provided for pre-processing data by subsampling or decorrelation. Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011,@sklearn2013api] (https://scikit-learn.org). *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. @@ -145,7 +145,7 @@ Some work on alchemlyb was supported by grants from the National Institutes of # Author contributions -D.D. and O.B. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the codebase. Z.W., M.R.S, O.B. wrote the manuscript. +D.D. and O.B. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the code base. Z.W., M.R.S, O.B. wrote the manuscript. # References From e6c6023f5dba0521609e9e74283c2b8de34c2fbd Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 17:35:29 -0700 Subject: [PATCH 30/92] added David Mobley to authors TODO: @davidlmobley please check --- joss_paper/paper.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 2d11ab92..11e3b205 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -14,22 +14,27 @@ authors: orcid: 0000-0001-5879-2942 equal-contrib: true # (This is how you can denote equal contributions between multiple authors) affiliation: 2 + - name: David Mobley + orcid: 0000-0002-1083-5533 + affiliation: 3 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 - affiliation: 3 + affiliation: 4 - name: Oliver Beckstein orcid: 0000-0003-1340-0831 corresponding: true # (This is how to denote the corresponding author) - affiliation: 4 + affiliation: 5 affiliations: - name: Exscientia, Oxford, UK index: 1 - - name: Datryllic LLC, Phoenix, AZ, USA + - name: Datryllic LLC, Phoenix, Arizona, USA index: 2 - - name: University of Colorado Boulder, Boulder, Colorado, USA + - name: University of California Irvine, Irvine, California, USA index: 3 - - name: Arizona State University, Tempe, Arizona, USA + - name: University of Colorado Boulder, Boulder, Colorado, USA index: 4 + - name: Arizona State University, Tempe, Arizona, USA + index: 5 date: 31 May 2024 bibliography: paper.bib From ea900ca7e1926198fcf4a1ff7ef8cd0a2d209e7f Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 17:38:45 -0700 Subject: [PATCH 31/92] minor abstract edits --- joss_paper/paper.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 11e3b205..fb043a4a 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -43,10 +43,9 @@ bibliography: paper.bib # Summary -*alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an integral part of computational chemistry and biology, most notably in the field of drug discovery. -Its functionality covers contains individual function-based building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. - -*alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final result derivation. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to utilize *alchemlyb* effectively. +*alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an important method in computational chemistry and biology, most notably in the field of drug discovery. +Its functionality contains individual composable building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. +*alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final results. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to use *alchemlyb* effectively. # Statement of need From 98b829e4e4118ed30946c7bce100da5bf82dcfcd Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 17:43:48 -0700 Subject: [PATCH 32/92] updated author contribs Added MRS and DM to project design. --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index fb043a4a..10561c3a 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -149,7 +149,7 @@ Some work on alchemlyb was supported by grants from the National Institutes of # Author contributions -D.D. and O.B. designed the project. Z.W., D.D., contributed to the new features. Z.W., D.D., O.B. maintain the code base. Z.W., M.R.S, O.B. wrote the manuscript. +D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintain the code base. Z.W., M.R.S, O.B. wrote the manuscript. # References From 7541d7159bafdc128055ae3e38026c05ac711c94 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 17:51:07 -0700 Subject: [PATCH 33/92] fix citation syntax See https://pandoc.org/MANUAL.html#citation-syntax --- joss_paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 10561c3a..d27c35f8 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -58,13 +58,13 @@ Other free energies extracted from simulations are useful in solution thermodyna Molecular dynamics (MD) packages such as GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. -Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines,@Mey2020aa] or are out of date +Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. *alchemlyb* addresses this problem by focusing only on the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different MD packages. In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a *pandas* `DataFrame` [@mckinney-proc-scipy-2010] (https://pandas.pydata.org). Functions are provided for pre-processing data by subsampling or decorrelation. -Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011,@sklearn2013api] (https://scikit-learn.org). +Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011; @sklearn2013api] (https://scikit-learn.org). *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. *alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. @@ -118,7 +118,7 @@ Metadata such as $T$ and the energy unit are stored in DataFrame attributes and Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. Both types of estimators assume uncorrelated samples in order to give unbiased estimates of the uncertainties, which requires subsampling of the raw data. -The `alchemlyb.preprocessing.subsampling` module provides tools for data subsampling based on autocorrelation times [@chodera2007use,@Chodera2016aa] as well as simple slicing of the `dHdl` and `u_nk` DataFrames. +The `alchemlyb.preprocessing.subsampling` module provides tools for data subsampling based on autocorrelation times [@chodera2007use; @Chodera2016aa] as well as simple slicing of the `dHdl` and `u_nk` DataFrames. The two major classes of commonly used estimators are implemented in `alchemlyb.estimators`. Unlike other components of *alchemlyb* that are implemented as pure functions, estimators are implemented as classes and follow the well-known scikit-learn API [@sklearn2013api] where instantiation sets the parameters (e.g., `estimator = MBAR(maximum_iterations=10000)`) and calling of the `fit()` method (e.g., `estimator.fit(u_nk)`) applies the estimator to the data and populates output attributes of the class; these results attributes are customarily indicated with a trailing underscore (e.g., `estimator.delta_f_` for the matrix of free energy differences between all states). From dc99971645837adc23abffbf04dd8a2f37e0656f Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 18:02:01 -0700 Subject: [PATCH 34/92] minor edits --- joss_paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index d27c35f8..d82ae1e4 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -67,8 +67,8 @@ Functions are provided for pre-processing data by subsampling or decorrelation. Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011; @sklearn2013api] (https://scikit-learn.org). *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. -*alchemlyb* succeeds the widely-used but now deprecated [alchemical-analysis.py](https://github.com/MobleyLab/alchemical-analysis) tool [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. -`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, as well as remaining in python 2. +*alchemlyb* succeeds the widely-used but now deprecated `alchemical-analysis.py` tool (https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. +`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, and only supported outdated Python 2. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. From 82d45a06423041dda835c664b396ccb7eaaa2754 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 15 May 2024 18:06:34 -0700 Subject: [PATCH 35/92] added all other authors to acknowledgements source: AUTHORS file --- joss_paper/paper.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index d82ae1e4..563cb6c7 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -147,6 +147,8 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Thomas T. Joseph, Irfan Alibay, Pascal Merz, Domenico Marson, and Haoxi Li contributed code to *alchemlyb*. + # Author contributions D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintain the code base. Z.W., M.R.S, O.B. wrote the manuscript. From cf04861c3a2a156fb012b32a64cb3863aca19df8 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:26:14 -0700 Subject: [PATCH 36/92] changed Author contributions: maintenance Changed "maintain" to "maintained" to more accurately capture present AND past contributions by ZW, DD, OB. --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 563cb6c7..5aedc3b2 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -151,7 +151,7 @@ Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, # Author contributions -D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintain the code base. Z.W., M.R.S, O.B. wrote the manuscript. +D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintained the code base. Z.W., M.R.S, O.B. wrote the manuscript. # References From 3f39574255f99567d130b40f64361a00e4c4084a Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:32:36 -0700 Subject: [PATCH 37/92] funding ack State that funds were not involved, per guidelines/policies https://joss.readthedocs.io/en/latest/submitting.html#policies --- joss_paper/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 5aedc3b2..d753defe 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -145,7 +145,8 @@ It can directly estimate quantities such as solvation free energies and makes it # Acknowledgements -Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). +Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). +The sponsors were not involved in any aspects of the research or the writing of the manuscript. Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Thomas T. Joseph, Irfan Alibay, Pascal Merz, Domenico Marson, and Haoxi Li contributed code to *alchemlyb*. From 834ed8637d15efe950ba3073b38e20494f7a4743 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:35:39 -0700 Subject: [PATCH 38/92] minor edit (use MD after first definition) --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index d753defe..f313829b 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -88,7 +88,7 @@ Stratified alchemical free energy calculations have emerged as a de-facto standa In such free energy calculations, overlapping states are created by the introduction of a parameter $\lambda$ that continuously connects the functional form (the Hamiltonian of the system) of the two end-states, resulting in a series of intermediate states each with a different $\lambda$ value between 0 and 1 and with the physically realizable end states at $\lambda=0$ and $\lambda=1$. In general, $N$ alchemical parameters are used to describe the alchemical transformation with a parameter vector $\vec{\lambda}=(\lambda_1, \lambda_2, \dots, \lambda_N)$, so that $\vec{\lambda}=(0, 0, \dots, 0)$ indicates the initial and $\vec{\lambda} = (1, 1, \dots, 1)$ the final state. The intermediate states are non-physical but required for converging the calculations. -At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using molecular dynamics (MD) or Monte Carlo (MC) simulations, while generating and accumulating free energy data discussed below. +At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using MD or Monte Carlo (MC) simulations, while generating and accumulating free energy data discussed below. Estimators are then applied to these data to compute free energy differences between states, including the difference between the final and initial state, thus yielding the desired free energy difference of the physical process of interest. ## Core design principles From 7d565360fb1944acc39b00664ebe22575a8521bf Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:37:45 -0700 Subject: [PATCH 39/92] add ASU to @dotsdl affiliation --- joss_paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index f313829b..57e0d77e 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -13,7 +13,7 @@ authors: - name: David L. Dotson orcid: 0000-0001-5879-2942 equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - affiliation: 2 + affiliation: "2, 5" - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 3 @@ -27,7 +27,7 @@ authors: affiliations: - name: Exscientia, Oxford, UK index: 1 - - name: Datryllic LLC, Phoenix, Arizona, USA + - name: Datryllic LLC, Phoenix, Arizona, USA (current affiliation) index: 2 - name: University of California Irvine, Irvine, California, USA index: 3 From 3dad66f79e8e224837118aa617f3fe88175d1fd9 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:40:17 -0700 Subject: [PATCH 40/92] use referencing for figure --- joss_paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 57e0d77e..f646f17c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -130,9 +130,9 @@ The error of the TI method is correlated with the average curvature [@pham2011id *alchemlyb* creates visualizations the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators, which can be qualitatively and quantitatively analyzed to determine the degree of overlap between simulated alchemical states, and suggest whether additional simulations should be run. For statistical validity, the accumulated samples should be collected from equilibrated simulations and *alchemlyb* contains tools for assessing (`alchemlyb.convergence`) and plotting (`alchemlyb.visualisation`) the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. -*alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (Figure 1). +*alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (\autoref{fig:buildingblocks}). -![The building blocks of *alchemlyb*. Raw data from simulation packages are parsed into common data structures depending on the free energy quantities, pre-processed, and processed with a free energy estimator. The resulting free energy differences are analyzed for convergence and plotted for quality assessment.](Fig1.pdf) +![The building blocks of *alchemlyb*. Raw data from simulation packages are parsed into common data structures depending on the free energy quantities, pre-processed, and processed with a free energy estimator. The resulting free energy differences are analyzed for convergence and plotted for quality assessment.\label{fig:buildingblocks}](Fig1.pdf) ## Workflows From 950b1d82b15570bdd4df1ca2610cc1742d2ebfbc Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:50:34 -0700 Subject: [PATCH 41/92] add ASU to @dotsdl 's affiliation --- joss_paper/paper.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index f646f17c..6bdf02c9 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -12,28 +12,28 @@ authors: affiliation: 1 - name: David L. Dotson orcid: 0000-0001-5879-2942 - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - affiliation: "2, 5" + equal-contrib: true + affiliation: "2, 3" - name: David Mobley orcid: 0000-0002-1083-5533 - affiliation: 3 + affiliation: 4 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 - affiliation: 4 + affiliation: 5 - name: Oliver Beckstein orcid: 0000-0003-1340-0831 - corresponding: true # (This is how to denote the corresponding author) - affiliation: 5 + corresponding: true + affiliation: 2 affiliations: - name: Exscientia, Oxford, UK index: 1 - - name: Datryllic LLC, Phoenix, Arizona, USA (current affiliation) + - name: Arizona State University, Tempe, Arizona, USA index: 2 - - name: University of California Irvine, Irvine, California, USA + - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) index: 3 - - name: University of Colorado Boulder, Boulder, Colorado, USA + - name: University of California Irvine, Irvine, California, USA index: 4 - - name: Arizona State University, Tempe, Arizona, USA + - name: University of Colorado Boulder, Boulder, Colorado, USA index: 5 date: 31 May 2024 From bf9c5157cfd4b925292047bb80b7563d8da7b338 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 09:59:17 -0700 Subject: [PATCH 42/92] add @dotsdl to "wrote paper" in contributions as he was missing from there --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 6bdf02c9..83eaa83d 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -152,7 +152,7 @@ Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, # Author contributions -D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintained the code base. Z.W., M.R.S, O.B. wrote the manuscript. +D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintained the code base. Z.W., D.D., M.R.S, O.B. wrote the manuscript. # References From e3ee72c19f1b19b7258b29f317b43ef32e64d309 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 11:15:09 -0700 Subject: [PATCH 43/92] reference update/cleanup - use canonical reference for Amber (and write software name as "Amber" instead of "AMBER" as in the paper/website) - use more recent GROMACS reference - use software / JOSS citation for BioSimSpace (instead of tutorial reference) - use proper GOMC reference (note that the 2021 is the one containing free energy calculations) - added DOIs to references - fixed treatment of "Jr" --- joss_paper/paper.bib | 90 +++++++++++++++++++++++++------------------- joss_paper/paper.md | 8 ++-- 2 files changed, 56 insertions(+), 42 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 42df86fb..1e88776f 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -8,15 +8,19 @@ @article{bennett1976efficient year={1976}, } -@article{case2014ff14sb, - title={The FF14SB force field}, - author={Case, D. and Babin, V. and Berryman, J. and Betz, R. and Cai, Q. and Cerutti, D. and et al.}, - journal={Amber}, - volume={14}, - pages={29-31}, - year={2014}, + +@article{Case2005uq, + author = {Case, David A and Cheatham, 3rd, Thomas E and Darden, Tom and Gohlke, Holger and Luo, Ray and Merz, Jr, Kenneth M and Onufriev, Alexey and Simmerling, Carlos and Wang, Bing and Woods, Robert J}, + journal = {J Comput Chem}, + number = {16}, + pages = {1668-1688}, + title = {The {Amber} biomolecular simulation programs}, + volume = {26}, + year = {2005}, + doi = {10.1002/jcc.20290} } + @article{chodera2007use, title={Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations}, author={Chodera, J. D. and Swope, W. C. and Pitera, J. W. and Seok, C. and Dill, K. A.}, @@ -28,17 +32,18 @@ @article{chodera2007use doi={10.1021/ct0502864}, } -@article{cummings2021open, - title={Open-source molecular modeling software in chemical engineering focusing on the Molecular Simulation Design Framework}, - author={Cummings, P. T. and McCabe, C. and Iacovella, C. R. and Ledeczi, A. and Jankowski, E. and Jayaraman, A. and et al.}, - journal={AIChE Journal}, - volume={67}, - number={3}, - pages={e17206}, - year={2021}, - doi={https://doi.org/10.1002/aic.17206}, + +@article{Nejahi2021aa, + author = {Younes Nejahi and Mohammad Soroush Barhaghi and Gregory Schwing and Loren Schwiebert and Jeffrey Potoff}, + journal = {SoftwareX}, + pages = {100627}, + title = {Update 2.70 to ``{GOMC}: {GPU} Optimized {Monte Carlo} for the simulation of phase equilibria and physical properties of complex fluids''}, + volume = {13}, + doi = {10.1016/j.softx.2020.100627}, + year = {2021} } + @article{deng2009computations, title={Computations of standard binding free energies with molecular dynamics simulations}, author={Deng, Y. and Roux, B.}, @@ -61,15 +66,18 @@ @article{gusev2023active doi={10.1021/acs.jcim.2c01052}, } -@article{hedges2023suite, - title={A Suite of Tutorials for the BioSimSpace Framework for Interoperable Biomolecular Simulation [Article v1.0]}, - author={Hedges, L. O. and Bariami, S. and Burman, M. and Clark, F. and Cossins, B. P. and Hardie, A. and et al.}, - journal={Living Journal of Computational Molecular Science}, - volume={5}, - number={1}, - pages={2375}, - year={2023}, - doi={10.33011/livecoms.5.1.2375}, + +@article{Hedges2019aa, + author = {Lester O. Hedges and Antonia S.J.S. Mey and Charles A. Laughton and Francesco L. Gervasio and Adrian J. Mulholland and Christopher J. Woods and Julien Michel}, + journal = {Journal of Open Source Software}, + keywords = {free energy, framework, python, MD SIMULATION}, + number = {43}, + pages = {1831}, + publisher = {The Open Journal}, + title = {{BioSimSpace}: An interoperable {Python} framework for biomolecular simulation}, + volume = {4}, + year = {2019}, + doi = {10.21105/joss.01831} } @article{kirkwood1935statistical, @@ -95,7 +103,7 @@ @article{klimovich2015guidelines @book{merz2010drug, title={Drug design: structure-and ligand-based approaches}, - author={Merz Jr, K. M. and Ringe, D. and Reynolds, C. H.}, + author={Merz, Jr, K. M. and Ringe, D. and Reynolds, C. H.}, year={2010}, publisher={Cambridge University Press}, } @@ -144,15 +152,15 @@ @article{pohorille2010good doi={10.1021/jp102971x}, } -@article{pronk2013gromacs, - title={GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit}, - author={Pronk, S. and Páll, S. and Schulz, R. and Larsson, P. and Bjelkmar, P. and Apostolov, R. and et al.}, - journal={Bioinformatics}, - volume={29}, - number={7}, - pages={845-854}, - year={2013}, - doi={10.1093/bioinformatics/btt055}, + +@article{Abraham2015aa, + author = {Abraham, Mark James and Murtola, Teemu and Schulz, Roland and P{\'a}ll, Szil{\'a}rd and Smith, Jeremy C. and Hess, Berk and Lindahl, Erik}, + journal = {SoftwareX}, + pages = {19 - 25}, + title = {{GROMACS}: High performance molecular simulations through multi-level parallelism from laptops to supercomputers}, + volume = {1--2}, + year = {2015}, + doi = {10.1016/j.softx.2015.06.001} } @article{shirts2008statistically, @@ -193,7 +201,8 @@ @article{fan2020aa pages = {543--560}, title = {Prediction of octanol-water partition coefficients for the {SAMPL6}-{$\log P$} molecules using molecular dynamics simulations with {OPLS-AA}, {AMBER} and {CHARMM} force fields}, volume = {34}, - year = {2020} + year = {2020}, + doi = {10.1007/s10822-019-00267-z} } @@ -204,7 +213,8 @@ @article{Mey2020aa pages = {18378}, title = {Best Practices for Alchemical Free Energy Calculations}, volume = {2}, - year = {2020} + year = {2020}, + doi = {10.33011/livecoms.2.1.18378} } @@ -216,7 +226,9 @@ @article{Chodera2016aa pages = {1799--1805}, title = {A Simple Method for Automated Equilibration Detection in Molecular Simulations}, volume = {12}, - year = {2016}} + year = {2016}, + doi = {10.1021/acs.jctc.5b00784} +} @InProceedings{ mckinney-proc-scipy-2010, author = { {W}es {M}c{K}inney }, @@ -253,3 +265,5 @@ @inproceedings{sklearn2013api year = {2013}, pages = {108--122}, } + + \ No newline at end of file diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 83eaa83d..f664e377 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -44,7 +44,7 @@ bibliography: paper.bib # Summary *alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an important method in computational chemistry and biology, most notably in the field of drug discovery. -Its functionality contains individual composable building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular dynamics (MD) packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. +Its functionality contains individual composable building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular simulation packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. *alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final results. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to use *alchemlyb* effectively. @@ -55,7 +55,7 @@ Notably, absolute binding free energy calculations between proteins and ligands The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. -Molecular dynamics (MD) packages such as GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. +Molecular simulation packages such as GROMACS [@Abraham2015aa], Amber [@Case2005uq], NAMD [@phillips2020scalable], and GOMC [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date @@ -73,7 +73,7 @@ Statistical mechanical estimators are available to extract free energies and the Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. -Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@hedges2023suite] or MDPOW [@fan2020aa], demonstrating its accessibility and usability within broader scientific workflows and reinforcing its position as a versatile tool in the field of computational chemistry. +Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@Hedges2019aa] or MDPOW [@fan2020aa], demonstrating its accessibility and usability within broader scientific workflows and reinforcing its position as a versatile tool in the field of computational chemistry. # Implementation @@ -112,7 +112,7 @@ The guiding design principles are summarized as: ## Library structure -*alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various MD packages (GROMACS [@pronk2013gromacs], AMBER [@case2014ff14sb], NAMD [@phillips2020scalable], and GOMC [@cummings2021open]). +*alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various molecular simulation packages (GROMACS [@Abraham2015aa], Amber [@Case2005uq], NAMD [@phillips2020scalable], and GOMC [@Nejahi2021aa]). The raw data are converted into a standard format as a `pandas.DataFrame` and converted from the energy of the software to units of $k T$ where $k = 1.380649 \times 10^{-23}\,\text{J}\,\text{K}^{-1}$ is Boltzmann's constant and $T$ is the temperature at which the simulation was performed. Metadata such as $T$ and the energy unit are stored in DataFrame attributes and propagated through *alchemlyb*, which enables seamless unit conversion with functions in the `alchemlyb.postprocessing` module. Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. From 1ae84648a9d9f105171b283fca1c89e099d3edd9 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 11:28:30 -0700 Subject: [PATCH 44/92] use hyperlinks instead of bare URLs - attach all URLs to anchors - add URLs for software packages --- joss_paper/paper.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index f664e377..2896f62f 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -55,19 +55,19 @@ Notably, absolute binding free energy calculations between proteins and ligands The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. -Molecular simulation packages such as GROMACS [@Abraham2015aa], Amber [@Case2005uq], NAMD [@phillips2020scalable], and GOMC [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. +Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. *alchemlyb* addresses this problem by focusing only on the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different MD packages. -In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a *pandas* `DataFrame` [@mckinney-proc-scipy-2010] (https://pandas.pydata.org). +In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a [*pandas*](https://pandas.pydata.org) `DataFrame` [@mckinney-proc-scipy-2010]. Functions are provided for pre-processing data by subsampling or decorrelation. -Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in scikit-learn [@scikitlearn2011; @sklearn2013api] (https://scikit-learn.org). +Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in [scikit-learn](https://scikit-learn.org) [@scikitlearn2011; @sklearn2013api]. *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. -*alchemlyb* succeeds the widely-used but now deprecated `alchemical-analysis.py` tool (https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. +*alchemlyb* succeeds the widely-used but now deprecated [`alchemical-analysis.py` tool](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. `alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, and only supported outdated Python 2. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. @@ -99,7 +99,7 @@ Functions are simple in usage and pure in scope, and can be chained together to General and robust workflows following best practices are also provided, which can be used as reference implementations and examples. First and foremost, scientific code must be correct and we try to ensure this requirement by following best software engineering practices during development, close to full test coverage of all code in the library (currently 99%), and providing citations to published papers for included algorithms. -We use a curated, public data set (*alchemtest* (https://github.com/alchemistry/alchemtest)) for automated testing. +We use a curated, public data set ([*alchemtest*](https://github.com/alchemistry/alchemtest)) for automated testing; code in *alchemtest* is published under the open source BSD-3 clause license while all data are included under an [open license](https://opendefinition.org/licenses/#recommended-conformant-licenses) such as [CC0](https://creativecommons.org/publicdomain/zero/1.0/) (public domain) or [CC-BY](http://opendefinition.org/licenses/cc-by/) (attribution required). The guiding design principles are summarized as: @@ -112,7 +112,7 @@ The guiding design principles are summarized as: ## Library structure -*alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various molecular simulation packages (GROMACS [@Abraham2015aa], Amber [@Case2005uq], NAMD [@phillips2020scalable], and GOMC [@Nejahi2021aa]). +*alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various molecular simulation packages ([GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa]) and provides a general structure for implementing parsers for other packages that are not yet supported. The raw data are converted into a standard format as a `pandas.DataFrame` and converted from the energy of the software to units of $k T$ where $k = 1.380649 \times 10^{-23}\,\text{J}\,\text{K}^{-1}$ is Boltzmann's constant and $T$ is the temperature at which the simulation was performed. Metadata such as $T$ and the energy unit are stored in DataFrame attributes and propagated through *alchemlyb*, which enables seamless unit conversion with functions in the `alchemlyb.postprocessing` module. Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. @@ -123,7 +123,7 @@ The `alchemlyb.preprocessing.subsampling` module provides tools for data subsamp The two major classes of commonly used estimators are implemented in `alchemlyb.estimators`. Unlike other components of *alchemlyb* that are implemented as pure functions, estimators are implemented as classes and follow the well-known scikit-learn API [@sklearn2013api] where instantiation sets the parameters (e.g., `estimator = MBAR(maximum_iterations=10000)`) and calling of the `fit()` method (e.g., `estimator.fit(u_nk)`) applies the estimator to the data and populates output attributes of the class; these results attributes are customarily indicated with a trailing underscore (e.g., `estimator.delta_f_` for the matrix of free energy differences between all states). In *alchemlyb*, TI [@paliwal2011benchmark] and TI with Gaussian quadrature [@gusev2023active] estimators are implemented in the TI category of estimators (module `alchemlyb.estimators.TI`). -FEP category estimators (module `alchemlyb.estimators.FEP`) include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically], which are implemented in the `pymbar` package [@shirts2008statistically] (https://github.com/choderalab/pymbar) and called from *alchemlyb*. +FEP category estimators (module `alchemlyb.estimators.FEP`) include Bennett Acceptance Ratio (BAR) [@bennett1976efficient] and Multistate BAR (MBAR) [@shirts2008statistically], which are implemented in the [*pymbar*](https://github.com/choderalab/pymbar) package [@shirts2008statistically] and called from *alchemlyb*. To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. From a9809d28a9da817e98ca4bab3ba82d687d717e07 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 16 May 2024 11:33:07 -0700 Subject: [PATCH 45/92] update artifact upload action --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index ba0463b1..13ffa0ab 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -14,7 +14,7 @@ jobs: # This should be the path to the paper within your repo. paper-path: joss_paper/paper.md - name: Upload - uses: actions/upload-artifact@v1 + uses: actions/upload-artifact@v4 with: name: paper # This is the output path where Pandoc will write the compiled From 4d7996563efb57e3dd2017e4c69a8de8e054a415 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 21 May 2024 08:49:59 -0700 Subject: [PATCH 46/92] add PDF for author review --- joss_paper/paper_joss_20240520.pdf | Bin 0 -> 1159205 bytes 1 file changed, 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[PATCH 47/92] used DLD instead of DD for @dotsdl --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 2896f62f..73be95c6 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -152,7 +152,7 @@ Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, # Author contributions -D.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.D., contributed to new features. Z.W., D.D., O.B. maintained the code base. Z.W., D.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. # References From 6344006f9addf1858099c7b3f437e5832d0e676d Mon Sep 17 00:00:00 2001 From: Domenico Marson Date: Wed, 22 May 2024 08:27:05 +0200 Subject: [PATCH 48/92] added my name (#361) --- joss_paper/paper.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 73be95c6..0f988a6e 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -14,6 +14,9 @@ authors: orcid: 0000-0001-5879-2942 equal-contrib: true affiliation: "2, 3" + - name: Domenico Marson + orcid: 0000-0003-1839-9868 + affiliation: 6 - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 4 @@ -35,6 +38,8 @@ affiliations: index: 4 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 5 + - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy + index: 6 date: 31 May 2024 bibliography: paper.bib @@ -148,11 +153,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Thomas T. Joseph, Irfan Alibay, Pascal Merz, Domenico Marson, and Haoxi Li contributed code to *alchemlyb*. +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Thomas T. Joseph, Irfan Alibay, Pascal Merz, and Haoxi Li contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. # References From 4fc3ee56fd8d6423af9b55dcacc8c423d539c12b Mon Sep 17 00:00:00 2001 From: Tom Joseph Date: Wed, 22 May 2024 15:56:39 -0400 Subject: [PATCH 49/92] JOSS paper: Add T. Joseph to authors and grant to acknowledgements (#362) * Add T. Joseph to authors and grant to acknowledgements * Remove TTJ from Acknowledgements, add to Author Contributions --- joss_paper/paper.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 0f988a6e..175edbd3 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -17,6 +17,9 @@ authors: - name: Domenico Marson orcid: 0000-0003-1839-9868 affiliation: 6 + - name: Thomas T. Joseph + orcid: 0000-0003-1323-3244 + affiliation: 7 - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 4 @@ -40,6 +43,8 @@ affiliations: index: 5 - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 6 + - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA + index: 7 date: 31 May 2024 bibliography: paper.bib @@ -150,14 +155,14 @@ It can directly estimate quantities such as solvation free energies and makes it # Acknowledgements -Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B.) and the National Science Foundation (award ACI-1443054 to O.B.). +Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Thomas T. Joseph, Irfan Alibay, Pascal Merz, and Haoxi Li contributed code to *alchemlyb*. +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Irfan Alibay, Pascal Merz, and Haoxi Li contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. # References From adf5a26be0eeb03cb0d060e3b3eff3c55553cec0 Mon Sep 17 00:00:00 2001 From: hl2500 <49323647+hl2500@users.noreply.github.com> Date: Thu, 23 May 2024 08:28:37 -0400 Subject: [PATCH 50/92] add name and minor change (#363) --- joss_paper/paper.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 175edbd3..104410e9 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -20,6 +20,9 @@ authors: - name: Thomas T. Joseph orcid: 0000-0003-1323-3244 affiliation: 7 + - name: Haoxi Li + orcid: 0009-0004-8369-1042 + affiliation: 8 - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 4 @@ -45,6 +48,8 @@ affiliations: index: 6 - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA index: 7 + - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. + index: 8 date: 31 May 2024 bibliography: paper.bib @@ -105,7 +110,7 @@ Estimators are then applied to these data to compute free energy differences bet *alchemlyb* is a Python library that seeks to make doing alchemical free energy calculations easier and less error prone. It includes functionality for parsing data from file formats of widely used simulation packages, subsampling these data, and fitting these data with an estimator to obtain free energies. -Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as a classes that follow the tried-and-tested scikit-learn API. +Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as classes that follow the tried-and-tested scikit-learn API. General and robust workflows following best practices are also provided, which can be used as reference implementations and examples. First and foremost, scientific code must be correct and we try to ensure this requirement by following best software engineering practices during development, close to full test coverage of all code in the library (currently 99%), and providing citations to published papers for included algorithms. @@ -158,11 +163,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Irfan Alibay, Pascal Merz, and Haoxi Li contributed code to *alchemlyb*. +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. # References From f663f50e9514edad57e9683102a9613043987b43 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 27 May 2024 09:48:49 -0700 Subject: [PATCH 51/92] edits by Alexander Schlaich - add @schlaicha to authors - edits throughout, in particular emphasis on general simulation packages (not just MD) Co-authored-by: Alexander Schlaich --- joss_paper/paper.md | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 104410e9..1891d0de 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -23,6 +23,9 @@ authors: - name: Haoxi Li orcid: 0009-0004-8369-1042 affiliation: 8 + - name: Alexander Schlaich + orcid: 0000-0002-4250-363X + affiliation: 9 - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 4 @@ -51,6 +54,8 @@ affiliations: - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. index: 8 + - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany + index: 9 date: 31 May 2024 bibliography: paper.bib @@ -73,11 +78,11 @@ Other free energies extracted from simulations are useful in solution thermodyna Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. -Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date -Overall, the coupling between data generation and analysis in most MD packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. +Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date. +Overall, the coupling between data generation and analysis in most simulation packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. -*alchemlyb* addresses this problem by focusing only on the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different MD packages. -In an initial step data are read from the native MD package file formats and then organized into a common standard data structure, organized as a [*pandas*](https://pandas.pydata.org) `DataFrame` [@mckinney-proc-scipy-2010]. +*alchemlyb* addresses this problem by focusing only on the data analysis portion of this process with the goal to provide a unified interface for working with free energy data generated from different software packages. +In an initial step data are read from the native package file formats and then organized into a common standard data structure, organized as a [*pandas*](https://pandas.pydata.org) `DataFrame` [@mckinney-proc-scipy-2010]. Functions are provided for pre-processing data by subsampling or decorrelation. Statistical mechanical estimators are available to extract free energies and thermodynamic expectations as well associated metrics of quality; these estimators are implemented as classes with the same API as estimators in [scikit-learn](https://scikit-learn.org) [@scikitlearn2011; @sklearn2013api]. *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. @@ -86,7 +91,7 @@ Statistical mechanical estimators are available to extract free energies and the `alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, and only supported outdated Python 2. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. -This innovation enables consistent processing of free energy data from diverse MD packages, facilitating streamlined comparison and combination of results. +This innovation enables consistent processing of free energy data from diverse simulation packages, facilitating streamlined comparison and combination of results. Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@Hedges2019aa] or MDPOW [@fan2020aa], demonstrating its accessibility and usability within broader scientific workflows and reinforcing its position as a versatile tool in the field of computational chemistry. @@ -103,7 +108,7 @@ Stratified alchemical free energy calculations have emerged as a de-facto standa In such free energy calculations, overlapping states are created by the introduction of a parameter $\lambda$ that continuously connects the functional form (the Hamiltonian of the system) of the two end-states, resulting in a series of intermediate states each with a different $\lambda$ value between 0 and 1 and with the physically realizable end states at $\lambda=0$ and $\lambda=1$. In general, $N$ alchemical parameters are used to describe the alchemical transformation with a parameter vector $\vec{\lambda}=(\lambda_1, \lambda_2, \dots, \lambda_N)$, so that $\vec{\lambda}=(0, 0, \dots, 0)$ indicates the initial and $\vec{\lambda} = (1, 1, \dots, 1)$ the final state. The intermediate states are non-physical but required for converging the calculations. -At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using MD or Monte Carlo (MC) simulations, while generating and accumulating free energy data discussed below. +At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using Molecular Dynamics (MD) or Monte Carlo (MC) simulations, while generating and accumulating free energy data discussed below. Estimators are then applied to these data to compute free energy differences between states, including the difference between the final and initial state, thus yielding the desired free energy difference of the physical process of interest. ## Core design principles @@ -153,21 +158,21 @@ For statistical validity, the accumulated samples should be collected from equil ## Workflows The building blocks are sufficient to compute free energies from alchemical free energy simulations and assess their reliability. -*alchemlyb* also provides a structure to combined the building blocks into full end-to-end workflows (module `alchemlyb.workflows`). +*alchemlyb* also provides a structure to combine the building blocks into full end-to-end workflows (module `alchemlyb.workflows`). As an example, the `ABFE` workflow for absolute binding free energy estimation reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. It can directly estimate quantities such as solvation free energies and makes it easy to calculate more complex quantities such as absolute binding free energies (as the difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket). # Acknowledgements -Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). +Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Alexander Schlaich, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. # References From 4a0d8921c5faa1f1c9bd123333d31228acdd26b4 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 27 May 2024 10:05:39 -0700 Subject: [PATCH 52/92] additional edis suggested by @schlaicha - add LAMMPS to list of packages - add more references (LAMMPS, paper for free energy in materials) - only talk about MD later, keep it initially general --- joss_paper/paper.bib | 23 ++++++++++++++++++++++- joss_paper/paper.md | 8 ++++---- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 1e88776f..6868e885 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -266,4 +266,25 @@ @inproceedings{sklearn2013api pages = {108--122}, } - \ No newline at end of file + +@incollection{Schlaich2015aa, + address = {J{\"u}lich}, + author = {Schlaich, A and Kowalik, B and Kandu{\v{c}}, M and Schneck, Emanuel and Netz, RR}, + booktitle = {Computational Trends in Solvation and Transport in Liquids-Lecture Notes (IAS Series 28)}, + editor = {Sutmann, G. and Grotendorst, J. and Gompper, G. and Marx, D.}, + pages = {155--185}, + publisher = {Forschungszentrum J{\"u}lich GmbH}, + title = {Simulation techniques for solvation-induced surface-interactions at prescribed water chemical potential}, + volume = 28, + year = 2015 +} + + +@article{Thompson2022aa, + author = {Aidan P. Thompson and H. Metin Aktulga and Richard Berger and Dan S. Bolintineanu and W. Michael Brown and Paul S. Crozier and Pieter J. {in 't Veld} and Axel Kohlmeyer and Stan G. Moore and Trung Dac Nguyen and Ray Shan and Mark J. Stevens and Julien Tranchida and Christian Trott and Steven J. Plimpton}, + journal = {Computer Physics Communications}, + pages = 108171, + title = {{LAMMPS} - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales}, + volume = 271, + year = 2022 +} diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 1891d0de..ee9fff5e 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -22,7 +22,7 @@ authors: affiliation: 7 - name: Haoxi Li orcid: 0009-0004-8369-1042 - affiliation: 8 + affiliation: 8 - name: Alexander Schlaich orcid: 0000-0002-4250-363X affiliation: 9 @@ -73,11 +73,11 @@ Its functionality contains individual composable building blocks for all aspects In the pharmaceutical sector, computational chemistry techniques are integral for evaluating potential drug compounds based on their protein binding affinity [@deng2009computations]. Notably, absolute binding free energy calculations between proteins and ligands or relative binding affinity of ligands to the same protein are routinely employed for this purpose [@merz2010drug]. The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. -Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science. +Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science [@Schlaich2015aa]. -Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. +Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], [LAMPPS](https://lammps.org/) [@Thompson2022aa], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. -Therefore, it is very difficult to analyze data from different MD packages in a consistent manner. +Therefore, it is very difficult to analyze data from different simulation packages in a consistent manner. Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date. Overall, the coupling between data generation and analysis in most simulation packages hinders seamless collaboration and comparison of results across different implementations of data generation for free energy calculations. From 0a4291b4cd14c93ab8cfad751535342b9a6be50b Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 27 May 2024 10:05:59 -0700 Subject: [PATCH 53/92] add @schlaicha to paper writers --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index ee9fff5e..88e216d6 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -172,7 +172,7 @@ Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From bc3b7fb2a48b8ac8fa3c9122fac423957186bf65 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 27 May 2024 10:21:29 -0700 Subject: [PATCH 54/92] name the JOSS paper build workflow --- .github/workflows/draft-pdf.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 13ffa0ab..10636716 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -1,3 +1,5 @@ +name: JOSS paper build + on: [push] jobs: From 1f01ec75f641d7f877ba5052b6d86525e1c20894 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20H=C3=A9nin?= Date: Tue, 28 May 2024 16:19:12 +0200 Subject: [PATCH 55/92] Add JH to author lists, extra reference (#365) * Add mention of SAFEP * Add JH to author list, first SAFEP reference also reorder middle authors alphabetically. --------- Co-authored-by: Tom Joseph --- joss_paper/paper.bib | 26 ++++++++++++++++++++++++++ joss_paper/paper.md | 37 ++++++++++++++++++++++--------------- 2 files changed, 48 insertions(+), 15 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 6868e885..77925ba1 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -288,3 +288,29 @@ @article{Thompson2022aa volume = 271, year = 2022 } + + +@Article{Salari2018, + author = {Salari, Reza and Joseph, Thomas and Lohia, Ruchi and Hénin, Jérôme and Brannigan, Grace}, + title = {A Streamlined, General Approach for Computing Ligand Binding Free Energies and Its Application to GPCR-Bound Cholesterol.}, + journal = {Journal of Chemical Theory and Computation}, + year = {2018}, + volume = {14}, + number = {12}, + pages = {6560--6573}, + doi = {10.1021/acs.jctc.8b00447}, +} + + +@article{santiagomcrae2023, + author={Santiago-McRae, E., and Ebrahimi, M. and Sandberg, J. W. and Brannigan, G. and Hénin, J.}, + title={Computing Absolute Binding Affinities by Streamlined Alchemical Free Energy Perturbation (SAFEP) [Article v1.0]}, + url={https://livecomsjournal.org/index.php/livecoms/article/view/v5i1e2067}, + doi={10.33011/livecoms.5.1.2067}, + journal={Living Journal of Computational Molecular Science}, + year={2023}, + month={Oct.}, + volume={5}, + number={1}, + pages={2067} +} diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 88e216d6..e222b25d 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -14,24 +14,27 @@ authors: orcid: 0000-0001-5879-2942 equal-contrib: true affiliation: "2, 3" - - name: Domenico Marson - orcid: 0000-0003-1839-9868 - affiliation: 6 + - name: Jérôme Hénin + orcid: 0000-0003-2540-4098 + affiliation: 4 - name: Thomas T. Joseph orcid: 0000-0003-1323-3244 - affiliation: 7 + affiliation: 5 - name: Haoxi Li orcid: 0009-0004-8369-1042 - affiliation: 8 + affiliation: 6 + - name: Domenico Marson + orcid: 0000-0003-1839-9868 + affiliation: 7 - name: Alexander Schlaich orcid: 0000-0002-4250-363X - affiliation: 9 + affiliation: 8 - name: David Mobley orcid: 0000-0002-1083-5533 - affiliation: 4 + affiliation: 9 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 - affiliation: 5 + affiliation: 10 - name: Oliver Beckstein orcid: 0000-0003-1340-0831 corresponding: true @@ -43,19 +46,21 @@ affiliations: index: 2 - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) index: 3 - - name: University of California Irvine, Irvine, California, USA + - name: Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France index: 4 - - name: University of Colorado Boulder, Boulder, Colorado, USA + - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA index: 5 - - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy + - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. index: 6 - - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA + - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 7 - - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. - index: 8 - - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany + index: 8 + - name: University of California Irvine, Irvine, California, USA index: 9 + - name: University of Colorado Boulder, Boulder, Colorado, USA + index: 10 + date: 31 May 2024 bibliography: paper.bib @@ -158,6 +163,8 @@ For statistical validity, the accumulated samples should be collected from equil ## Workflows The building blocks are sufficient to compute free energies from alchemical free energy simulations and assess their reliability. +This functionality is used, for example, by the Streamlined Alchemical Free Energy Perturbation (SAFEP) analysis scripts [@Salari2018; @santiagomcrae2023]. + *alchemlyb* also provides a structure to combine the building blocks into full end-to-end workflows (module `alchemlyb.workflows`). As an example, the `ABFE` workflow for absolute binding free energy estimation reads in the raw input data and performs decorrelation, estimation, and quality plotting of the estimate. It can directly estimate quantities such as solvation free energies and makes it easy to calculate more complex quantities such as absolute binding free energies (as the difference between the solvation free energy of the ligand in water and the solvation free energy of the ligand in the protein's binding pocket). From b7b15cbea7da86cc643a6ac76677201f45d2a319 Mon Sep 17 00:00:00 2001 From: Hyungro Lee Date: Tue, 28 May 2024 08:30:10 -0700 Subject: [PATCH 56/92] Add HL to author list with affiliation (#366) * Add HL to author list with affiliation * update * update --- joss_paper/paper.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index e222b25d..edd8f5f0 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -20,6 +20,9 @@ authors: - name: Thomas T. Joseph orcid: 0000-0003-1323-3244 affiliation: 5 + - name: Hyungro Lee + orcid: 0000-0002-4221-7094 + affiliation: 11 - name: Haoxi Li orcid: 0009-0004-8369-1042 affiliation: 6 @@ -60,6 +63,8 @@ affiliations: index: 9 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 10 + - name: Pacific Northwest National Laboratory, Richland, Washington, USA + index: 11 date: 31 May 2024 bibliography: paper.bib @@ -175,11 +180,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Hyungro Lee, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. +Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 1161e03711ca2f536d38ca4f6cf92a33ee570fcd Mon Sep 17 00:00:00 2001 From: Ian Kenney Date: Tue, 28 May 2024 08:50:34 -0700 Subject: [PATCH 57/92] Add Ian Kenney to author list (#367) * Added Ian M. Kenney to author list * Added I.M.K. to author contributions --- joss_paper/paper.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index edd8f5f0..25045311 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -20,6 +20,9 @@ authors: - name: Thomas T. Joseph orcid: 0000-0003-1323-3244 affiliation: 5 + - name: Ian M. Kenney + orchid: 0000-0002-9749-8866 + affiliation: 2 - name: Hyungro Lee orcid: 0000-0002-4221-7094 affiliation: 11 @@ -180,11 +183,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Ian Kenney, Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., H.L., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., I.M.K., H.L., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 6260d218fc21c1c4f26e57ddf87dc1901fccaf1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20H=C3=A9nin?= Date: Tue, 28 May 2024 20:51:18 +0200 Subject: [PATCH 58/92] Move JH from Acknowledgments to Contributions (#368) --- joss_paper/paper.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 25045311..7560e844 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -183,12 +183,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Jérôme Hénin, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., D.M., T.T.J., I.M.K., H.L., H.L., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. - +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., J.H., T.T.J., I.M.K., H.L., H.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 29aee32591b526bf390a0d1460176938ddee5507 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?J=C3=A9r=C3=B4me=20H=C3=A9nin?= Date: Tue, 28 May 2024 12:56:34 -0700 Subject: [PATCH 59/92] fixed typo LAMPPS -> LAMMPS --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 7560e844..ea284693 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -88,7 +88,7 @@ Notably, absolute binding free energy calculations between proteins and ligands The resultant estimates of these free energies are essential for understanding binding affinity throughout various stages of drug discovery, such as hit identification and lead optimization [@merz2010drug]. Other free energies extracted from simulations are useful in solution thermodynamics, chemical engineering, environmental science, and material science [@Schlaich2015aa]. -Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], [LAMPPS](https://lammps.org/) [@Thompson2022aa], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. +Molecular simulation packages such as [GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], [LAMMPS](https://lammps.org/) [@Thompson2022aa], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa] are used to run free energy simulations and many of these packages also contain tools for the subsequent processing of simulation data into free energies. However, there are no standard output formats and analysis tools implement different algorithms for the different stages of the free energy data processing pipeline. Therefore, it is very difficult to analyze data from different simulation packages in a consistent manner. Furthermore, the native analysis tools do not always implement current best practices [@klimovich2015guidelines; @Mey2020aa] or are out of date. From ae2bd7cfd9c92d9e127dad4918f25488398a23f2 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 28 May 2024 13:36:58 -0700 Subject: [PATCH 60/92] fiddle with bib to make JC Chodera citations appear unformly --- joss_paper/paper.bib | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 77925ba1..3cbd8fa1 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -23,7 +23,7 @@ @article{Case2005uq @article{chodera2007use, title={Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations}, - author={Chodera, J. D. and Swope, W. C. and Pitera, J. W. and Seok, C. and Dill, K. A.}, + author={J. D. Chodera and Swope, W. C. and Pitera, J. W. and Seok, C. and Dill, K. A.}, journal={J Chem Theory Comput}, volume={3}, number={1}, @@ -219,7 +219,7 @@ @article{Mey2020aa @article{Chodera2016aa, - author = {Chodera, John D.}, + author = {J. D. Chodera}, journal = {Journal of Chemical Theory and Computation}, month = {Apr}, number = {4}, From 1c1bc713abd1bdd1f8133574332a8bd54fc97736 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 28 May 2024 13:42:16 -0700 Subject: [PATCH 61/92] software engineering details and minor touch-ups - state that we follow NEP29 (transition to SPEC0, which is basically the same for us) - state that we use SemVer - minor text fixes throughout and adding of citations where needed --- joss_paper/paper.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index ea284693..95312fd7 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -128,7 +128,7 @@ Estimators are then applied to these data to compute free energy differences bet *alchemlyb* is a Python library that seeks to make doing alchemical free energy calculations easier and less error prone. It includes functionality for parsing data from file formats of widely used simulation packages, subsampling these data, and fitting these data with an estimator to obtain free energies. -Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as classes that follow the tried-and-tested scikit-learn API. +Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as classes that follow the tried-and-tested scikit-learn API [@sklearn2013api]. General and robust workflows following best practices are also provided, which can be used as reference implementations and examples. First and foremost, scientific code must be correct and we try to ensure this requirement by following best software engineering practices during development, close to full test coverage of all code in the library (currently 99%), and providing citations to published papers for included algorithms. @@ -141,13 +141,15 @@ The guiding design principles are summarized as: 3. Aim for a consistent interface throughout, e.g. all parsers take similar inputs and yield a common set of outputs, using the `pandas.DataFrame` as the underlying data structure. 4. Have *all* functionality tested. -*alchemlyb* is published under the open source BSD-3 clause license. +*alchemlyb* supports recent versions of Python 3 and follows the [NEP 29 (Recommend Python and NumPy version support as a community policy standard)](https://numpy.org/neps/nep-0029-deprecation_policy.html) standard (transitioning to the [SPEC 0 (Minimum Supported Dependencies)](https://scientific-python.org/specs/spec-0000/) Scientific Python Ecosystem Coordination community standard) for deciding on when to drop support for older versions of Python and dependencies. +Releases are numbered following the [Semantic Versioning 2.0.0](https://semver.org/) standard of MAJOR.MINOR.PATCHLEVEL, which ensures that users immediately understand if a release may break backwards compatibility (increase of the major version), adds new features (increase of minor version), or only contains bug fixes or other changes that do not directly affect users. +All code is published under the open source BSD-3 clause license. ## Library structure *alchemlyb* offers specific parsers in `alchemlyb.parsing` to load raw free energy data from various molecular simulation packages ([GROMACS](https://www.gromacs.org/) [@Abraham2015aa], [Amber](https://ambermd.org/) [@Case2005uq], [NAMD](https://www.ks.uiuc.edu/Research/namd/) [@phillips2020scalable], and [GOMC](https://gomc-wsu.org/) [@Nejahi2021aa]) and provides a general structure for implementing parsers for other packages that are not yet supported. -The raw data are converted into a standard format as a `pandas.DataFrame` and converted from the energy of the software to units of $k T$ where $k = 1.380649 \times 10^{-23}\,\text{J}\,\text{K}^{-1}$ is Boltzmann's constant and $T$ is the temperature at which the simulation was performed. -Metadata such as $T$ and the energy unit are stored in DataFrame attributes and propagated through *alchemlyb*, which enables seamless unit conversion with functions in the `alchemlyb.postprocessing` module. +The raw data are converted into a standard format as a `pandas.DataFrame` [@mckinney-proc-scipy-2010] and converted from the energy of the software to units of $k T$ where $k = 1.380649 \times 10^{-23}\,\text{J}\,\text{K}^{-1}$ is Boltzmann's constant and $T$ is the temperature at which the simulation was performed. +Metadata such as $T$ and the energy unit are stored in `DataFrame` attributes and propagated through *alchemlyb*, which enables seamless unit conversion with functions in the `alchemlyb.postprocessing` module. Two types of free energy data are considered: Hamiltonian gradients (`dHdl`, $dH/d\lambda$) at all lambda states, suitable for thermodynamic integration (TI) estimators [@kirkwood1935statistical], and reduced potential energy differences between lambda states (`u_nk`, $u_{nk}$), which are used for free energy perturbation (FEP) estimators [@zwanzig1954high]. Both types of estimators assume uncorrelated samples in order to give unbiased estimates of the uncertainties, which requires subsampling of the raw data. @@ -160,7 +162,7 @@ FEP category estimators (module `alchemlyb.estimators.FEP`) include Bennett Acce To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. -*alchemlyb* creates visualizations the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators, which can be qualitatively and quantitatively analyzed to determine the degree of overlap between simulated alchemical states, and suggest whether additional simulations should be run. +*alchemlyb* creates visualizations of the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators, which can be qualitatively and quantitatively analyzed to determine the degree of overlap between simulated alchemical states, and suggest whether additional simulations should be run. For statistical validity, the accumulated samples should be collected from equilibrated simulations and *alchemlyb* contains tools for assessing (`alchemlyb.convergence`) and plotting (`alchemlyb.visualisation`) the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (\autoref{fig:buildingblocks}). From be4c2e7a60afc635be15bc2d1808676a4f029c1f Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 28 May 2024 15:30:06 -0700 Subject: [PATCH 62/92] update @orbeckst's affiliation --- joss_paper/paper.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 95312fd7..58075818 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -44,11 +44,11 @@ authors: - name: Oliver Beckstein orcid: 0000-0003-1340-0831 corresponding: true - affiliation: 2 + affiliation: "2,12" affiliations: - name: Exscientia, Oxford, UK index: 1 - - name: Arizona State University, Tempe, Arizona, USA + - name: Department of Physics, Arizona State University, Tempe, Arizona, USA index: 2 - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) index: 3 @@ -68,6 +68,8 @@ affiliations: index: 10 - name: Pacific Northwest National Laboratory, Richland, Washington, USA index: 11 + - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA + index: 12 date: 31 May 2024 bibliography: paper.bib From 9b2657e7d11aea144c2a7b054fa572c7adb5a9e0 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 29 May 2024 08:32:22 -0700 Subject: [PATCH 63/92] using SPEC 0 (updated from NEP29) Changed with #369 --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 58075818..5e66f13c 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -143,7 +143,7 @@ The guiding design principles are summarized as: 3. Aim for a consistent interface throughout, e.g. all parsers take similar inputs and yield a common set of outputs, using the `pandas.DataFrame` as the underlying data structure. 4. Have *all* functionality tested. -*alchemlyb* supports recent versions of Python 3 and follows the [NEP 29 (Recommend Python and NumPy version support as a community policy standard)](https://numpy.org/neps/nep-0029-deprecation_policy.html) standard (transitioning to the [SPEC 0 (Minimum Supported Dependencies)](https://scientific-python.org/specs/spec-0000/) Scientific Python Ecosystem Coordination community standard) for deciding on when to drop support for older versions of Python and dependencies. +*alchemlyb* supports recent versions of Python 3 and follows the [SPEC 0 (Minimum Supported Dependencies)](https://scientific-python.org/specs/spec-0000/) Scientific Python Ecosystem Coordination community standard for deciding on when to drop support for older versions of Python and dependencies. Releases are numbered following the [Semantic Versioning 2.0.0](https://semver.org/) standard of MAJOR.MINOR.PATCHLEVEL, which ensures that users immediately understand if a release may break backwards compatibility (increase of the major version), adds new features (increase of minor version), or only contains bug fixes or other changes that do not directly affect users. All code is published under the open source BSD-3 clause license. From 00e4f102407fd80aef4d569d5832d5b42e9b0532 Mon Sep 17 00:00:00 2001 From: Irfan Alibay Date: Thu, 30 May 2024 14:30:40 +0100 Subject: [PATCH 64/92] Add Irfan Alibay to paper.md (#372) --- joss_paper/paper.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 5e66f13c..ab97f2ee 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -14,6 +14,9 @@ authors: orcid: 0000-0001-5879-2942 equal-contrib: true affiliation: "2, 3" + - name: Irfan Alibay + orcid: 0000-0001-5787-9130 + affiliation: 13 - name: Jérôme Hénin orcid: 0000-0003-2540-4098 affiliation: 4 @@ -21,7 +24,7 @@ authors: orcid: 0000-0003-1323-3244 affiliation: 5 - name: Ian M. Kenney - orchid: 0000-0002-9749-8866 + orcid: 0000-0002-9749-8866 affiliation: 2 - name: Hyungro Lee orcid: 0000-0002-4221-7094 @@ -70,6 +73,8 @@ affiliations: index: 11 - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA index: 12 + - name: Open Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States + index: 13 date: 31 May 2024 bibliography: paper.bib @@ -187,11 +192,11 @@ It can directly estimate quantities such as solvation free energies and makes it Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, Irfan Alibay, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., J.H., T.T.J., I.M.K., H.L., H.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., J.H., T.T.J., I.M.K., H.L., H.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From d9df3e48c4c0532e7e3a75ee7a5bd0e7fb8f3bf7 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 30 May 2024 10:58:44 -0700 Subject: [PATCH 65/92] PDF draft of paper as of 2024-05-30 REMOVE BEFORE SUBMISSION --- joss_paper/paper_joss_20240530.pdf | Bin 0 -> 263594 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 joss_paper/paper_joss_20240530.pdf diff --git a/joss_paper/paper_joss_20240530.pdf b/joss_paper/paper_joss_20240530.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eb7315605b5cfd899614a7f86555c896e6436ded GIT binary patch literal 263594 zcmaHRV~{98v*p;f?YU#ywtdI8ZQHi3JGO1xwymA-y^Yw7SoELn=#J`Ao|7lDl2l$; zl!k$p1&VZXsc&&;b#`zVikX0(z}CP5ikq8&PL_a)fKJrh%E{Q__im-{WGrlKXlrCl zKqqZ%W9no^z{o~VPr%Dd@ZT4dqmzTNzBQEFdUXo8GV&VU@XcgV;w%zTc0L=V41u4x zEs$bSq@*|yNCE(1{wj1NR22mgQ69xVs{MdUY643U6zE93zH2ZI`Up1pr}#auTXp@Mt-4;cy53VK^drU``wx 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ztvA}v)MQUlGZLQJdFDu?z;Oo_^+jQ#(r0Gft(TCVF!O{DRL&($N$;b>QD)J?&K*a- zuDbl@TlPj#|EQE@Y@4-%4Idxu_H$jX6gY8()Xv0c-P}{|OtTY|Wt#k6G)yjFIhM#I z=EN2h!6zzEzV`H+XEiyy7{nYUr#P$XwCy-QhqX}bNTfizW67$x2NE6cLYwL&7^JQ% z@EkOF!}v(3uydVD_@kUm9u1!*IzeKZ-BVmoDWy)_kW|YUJJr6%N*WA7&&i479uWiMdw$ literal 0 HcmV?d00001 From 4bcf9599d12c76d6a743e42ec3c8c3b8b6adca72 Mon Sep 17 00:00:00 2001 From: David Mobley Date: Thu, 30 May 2024 16:12:22 -0700 Subject: [PATCH 66/92] update information for David Mobley - add department - add grant information --- joss_paper/paper.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index ab97f2ee..0de3d74e 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -65,7 +65,7 @@ affiliations: index: 7 - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany index: 8 - - name: University of California Irvine, Irvine, California, USA + - name: Departments of Pharmaceutical Sciences and Chemistr, University of California Irvine, Irvine, California, USA index: 9 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 10 @@ -189,7 +189,9 @@ It can directly estimate quantities such as solvation free energies and makes it # Acknowledgements -Some work on alchemlyb was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). +Some work on alchemlyb was supported by grants from the National +Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to +D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. From 19c963c761a69f70aa8430cf554461cb42a4d455 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 30 May 2024 21:27:18 -0700 Subject: [PATCH 67/92] fix DM affiliation Co-authored-by: Ian Kenney --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 0de3d74e..a6484770 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -65,7 +65,7 @@ affiliations: index: 7 - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany index: 8 - - name: Departments of Pharmaceutical Sciences and Chemistr, University of California Irvine, Irvine, California, USA + - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA index: 9 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 10 From 767e89f5f52cc8c6e72ea80daa73c0cca52d5ec0 Mon Sep 17 00:00:00 2001 From: Victoria Lim Date: Thu, 30 May 2024 21:34:32 -0700 Subject: [PATCH 68/92] add @vtlim to Author list --- joss_paper/paper.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index a6484770..be908a29 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -32,6 +32,9 @@ authors: - name: Haoxi Li orcid: 0009-0004-8369-1042 affiliation: 6 + - name: Victoria Lim + orcid: 0000-0003-4030-9312 + affiliation: 9 - name: Domenico Marson orcid: 0000-0003-1839-9868 affiliation: 7 @@ -194,11 +197,11 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Victoria Lim, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., J.H., T.T.J., I.M.K., H.L., H.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 5fe9ec999bbf799c9ab38bce06285c9e2f770bb5 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Mon, 3 Jun 2024 19:59:05 +0100 Subject: [PATCH 69/92] Update joss_paper/paper.md Co-authored-by: Pascal Merz --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index be908a29..242d3079 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -111,7 +111,7 @@ Statistical mechanical estimators are available to extract free energies and the *alchemlyb* implements modular building blocks to simplify the process of extracting crucial thermodynamic insights from molecular simulations in a uniform manner. *alchemlyb* succeeds the widely-used but now deprecated [`alchemical-analysis.py` tool](https://github.com/MobleyLab/alchemical-analysis) [@klimovich2015guidelines], which combined pre-processing, free energy estimation, and plotting in a single script. -`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, and only supported outdated Python 2. +`alchemical-analysis.py` was not thoroughly tested and hard to integrate into modern workflows due to its monolithic design, and only supported (now outdated) Python 2. *alchemlyb* improves over its predecessor with a modular, function based design and thorough testing of all components using continuous integration. Thus, *alchemlyb* is a library that enables users to easily use well-tested building blocks within their own tools while additionally providing examples of complete end-to-end workflows. This innovation enables consistent processing of free energy data from diverse simulation packages, facilitating streamlined comparison and combination of results. From 41285c3e8f69ca7f3e687c25854bc4df8f6bcc6e Mon Sep 17 00:00:00 2001 From: Bryce Allen <31014151+brycestx@users.noreply.github.com> Date: Tue, 4 Jun 2024 00:21:05 -0700 Subject: [PATCH 70/92] Add affiliation (#374) * Add affiliation * change the order --------- Co-authored-by: Zhiyi Wu --- joss_paper/paper.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 242d3079..31f1a35f 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -17,6 +17,9 @@ authors: - name: Irfan Alibay orcid: 0000-0001-5787-9130 affiliation: 13 + - name: Bryce K. Allen + orcid: 0000-0002-0804-8127 + affiliation: 14 - name: Jérôme Hénin orcid: 0000-0003-2540-4098 affiliation: 4 @@ -78,6 +81,8 @@ affiliations: index: 12 - name: Open Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States index: 13 + - name: Differentiated Therapeutics, San Diego, CA + index: 14 date: 31 May 2024 bibliography: paper.bib @@ -197,11 +202,11 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Bryce Allen, Dominik Wille, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Dominik Wille, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From bc46b48170e27e0bcd9c52b8e562dd3770f13124 Mon Sep 17 00:00:00 2001 From: Pascal Merz Date: Tue, 4 Jun 2024 03:24:25 -0400 Subject: [PATCH 71/92] Update paper.md (#373) Co-authored-by: Zhiyi Wu --- joss_paper/paper.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 31f1a35f..03498fa6 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -41,6 +41,9 @@ authors: - name: Domenico Marson orcid: 0000-0003-1839-9868 affiliation: 7 + - name: Pascal T. Merz + orcid: 0000-0002-7045-8725 + affiliation: 15 - name: Alexander Schlaich orcid: 0000-0002-4250-363X affiliation: 8 @@ -83,6 +86,9 @@ affiliations: index: 13 - name: Differentiated Therapeutics, San Diego, CA index: 14 + - name: PM Scientific Consulting, Basel, Switzerland + index: 15 + date: 31 May 2024 bibliography: paper.bib @@ -202,11 +208,11 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Dominik Wille, Mohammad S. Barhaghi, and Pascal Merz contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen, Dominik Wille, and Mohammad S. Barhaghi contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 74d164a6eca3041625e35970e6ba4ba96d4dbbff Mon Sep 17 00:00:00 2001 From: Mohammad Soroush Barhaghi Date: Tue, 4 Jun 2024 14:33:29 +0200 Subject: [PATCH 72/92] Update paper.md for authorship (#375) * Update paper.md for authorship * revert the deleted names * fix the format --------- Co-authored-by: Zhiyi Wu --- joss_paper/paper.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 03498fa6..165109b8 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -20,6 +20,9 @@ authors: - name: Bryce K. Allen orcid: 0000-0002-0804-8127 affiliation: 14 + - name: Mohammad Soroush Barhaghi + orcid: 0000-0001-8226-7347 + affiliation: 16 - name: Jérôme Hénin orcid: 0000-0003-2540-4098 affiliation: 4 @@ -88,7 +91,8 @@ affiliations: index: 14 - name: PM Scientific Consulting, Basel, Switzerland index: 15 - + - name: Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan, USA + index: 16 date: 31 May 2024 bibliography: paper.bib @@ -208,11 +212,11 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen, Dominik Wille, and Mohammad S. Barhaghi contributed code to *alchemlyb*. +Shuai Liu, Travis Jensen and Dominik Wille contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., M.S.B, J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. # References From 388dc54ad29063601ea76d8c3b40f527163f6e55 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 15:58:38 -0700 Subject: [PATCH 73/92] finalized author information and date - re-ordered affiliations to increase numerically in first occurrence in author order - updated date to today --- joss_paper/paper.md | 58 ++++++++++++++++++++++----------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 165109b8..815d79cd 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -16,50 +16,50 @@ authors: affiliation: "2, 3" - name: Irfan Alibay orcid: 0000-0001-5787-9130 - affiliation: 13 + affiliation: 4 - name: Bryce K. Allen orcid: 0000-0002-0804-8127 - affiliation: 14 + affiliation: 5 - name: Mohammad Soroush Barhaghi orcid: 0000-0001-8226-7347 - affiliation: 16 + affiliation: 6 - name: Jérôme Hénin orcid: 0000-0003-2540-4098 - affiliation: 4 + affiliation: 7 - name: Thomas T. Joseph orcid: 0000-0003-1323-3244 - affiliation: 5 + affiliation: 8 - name: Ian M. Kenney orcid: 0000-0002-9749-8866 affiliation: 2 - name: Hyungro Lee orcid: 0000-0002-4221-7094 - affiliation: 11 + affiliation: 9 - name: Haoxi Li orcid: 0009-0004-8369-1042 - affiliation: 6 + affiliation: 10 - name: Victoria Lim orcid: 0000-0003-4030-9312 - affiliation: 9 + affiliation: 11 - name: Domenico Marson orcid: 0000-0003-1839-9868 - affiliation: 7 + affiliation: 12 - name: Pascal T. Merz orcid: 0000-0002-7045-8725 - affiliation: 15 + affiliation: 13 - name: Alexander Schlaich orcid: 0000-0002-4250-363X - affiliation: 8 + affiliation: 14 - name: David Mobley orcid: 0000-0002-1083-5533 - affiliation: 9 + affiliation: 11 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 - affiliation: 10 + affiliation: 15 - name: Oliver Beckstein orcid: 0000-0003-1340-0831 corresponding: true - affiliation: "2,12" + affiliation: "2,16" affiliations: - name: Exscientia, Oxford, UK index: 1 @@ -67,34 +67,34 @@ affiliations: index: 2 - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) index: 3 - - name: Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France + - name: Open Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States index: 4 - - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA + - name: Differentiated Therapeutics, San Diego, CA index: 5 - - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. + - name: Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan, USA index: 6 - - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy + - name: Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France index: 7 - - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany + - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA index: 8 - - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA + - name: Pacific Northwest National Laboratory, Richland, Washington, USA index: 9 - - name: University of Colorado Boulder, Boulder, Colorado, USA +- name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. index: 10 - - name: Pacific Northwest National Laboratory, Richland, Washington, USA - index: 11 - - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA + - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA + index: 11 #9 + - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 12 - - name: Open Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States + - name: PM Scientific Consulting, Basel, Switzerland index: 13 - - name: Differentiated Therapeutics, San Diego, CA + - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany index: 14 - - name: PM Scientific Consulting, Basel, Switzerland + - name: University of Colorado Boulder, Boulder, Colorado, USA index: 15 - - name: Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan, USA + - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA index: 16 -date: 31 May 2024 +date: 4 June 2024 bibliography: paper.bib --- From 921569553da9084cca7cdd17b97181bdcf85d259 Mon Sep 17 00:00:00 2001 From: Pascal Merz Date: Tue, 4 Jun 2024 16:03:19 -0700 Subject: [PATCH 74/92] introduce alchemical free energy calcs outside Implementation section - new section on background - move Implementation right before Core design principles Follows https://github.com/alchemistry/alchemlyb/pull/328#discussion_r1624458586 --- joss_paper/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 815d79cd..6b4186a5 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -134,7 +134,7 @@ This innovation enables consistent processing of free energy data from diverse s Notably, *alchemlyb*'s robust and user-friendly nature has led to its integration into other automated workflow libraries such as BioSimSpace [@Hedges2019aa] or MDPOW [@fan2020aa], demonstrating its accessibility and usability within broader scientific workflows and reinforcing its position as a versatile tool in the field of computational chemistry. -# Implementation +# Background: Alchemical free energy calculations Free energy differences are fundamental to understand many different processes at the molecular scale, ranging from the binding of drug molecules to their receptor proteins or nucleic acids through the partitioning of molecules into different solvents or phases to the stability of crystals and biomolecules. The calculation of such transfer free energies involves constructing two end states where a target molecule interacts with different environments. @@ -149,6 +149,7 @@ The intermediate states are non-physical but required for converging the calcula At each $\vec{\lambda}$-value (or "window"), the system configurations are sampled in the relevant thermodynamic ensemble, typically using Molecular Dynamics (MD) or Monte Carlo (MC) simulations, while generating and accumulating free energy data discussed below. Estimators are then applied to these data to compute free energy differences between states, including the difference between the final and initial state, thus yielding the desired free energy difference of the physical process of interest. +# Implementation ## Core design principles *alchemlyb* is a Python library that seeks to make doing alchemical free energy calculations easier and less error prone. From f9f5429d0fcd144f22d35a9a9361c541b9e786f5 Mon Sep 17 00:00:00 2001 From: Pascal Merz Date: Tue, 4 Jun 2024 18:21:46 -0700 Subject: [PATCH 75/92] edited paragraph on solvation free energy calc to improve clarity https://github.com/alchemistry/alchemlyb/pull/328#discussion_r1624478244 --- joss_paper/paper.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 6b4186a5..ece26025 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -138,9 +138,8 @@ Notably, *alchemlyb*'s robust and user-friendly nature has led to its integratio Free energy differences are fundamental to understand many different processes at the molecular scale, ranging from the binding of drug molecules to their receptor proteins or nucleic acids through the partitioning of molecules into different solvents or phases to the stability of crystals and biomolecules. The calculation of such transfer free energies involves constructing two end states where a target molecule interacts with different environments. -For example, in a solvation free energy calculation, at one state (the coupled state) it interacts with a solvent (in the case of hydration free energies, water), and the other where the ligand has no intermolecular interactions (the decoupled state), mimicking the transfer of a ligand at infinite dilution in the solvent at one end of the process and then ligand in the gas phase at the other. -The solvation free energy is then obtained by calculating the free energy difference between these two end states. -To achieve this, it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. +For example, in a solvation free energy calculation, in one state (the coupled state) the target molecule interacts with a solvent (in the case of hydration free energies, water), while in the other state (the decoupled state) the ligand has no intermolecular interactions, which mimics the transfer of a ligand from infinite dilution in the solvent to the gas phase. +The solvation free energy is then obtained by calculating the free energy difference between these two end states, but it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. Stratified alchemical free energy calculations have emerged as a de-facto standard approach whereby non-physical intermediate states are introduced to bridge between the physical end states of the process [@Mey2020aa]. In such free energy calculations, overlapping states are created by the introduction of a parameter $\lambda$ that continuously connects the functional form (the Hamiltonian of the system) of the two end-states, resulting in a series of intermediate states each with a different $\lambda$ value between 0 and 1 and with the physically realizable end states at $\lambda=0$ and $\lambda=1$. From 85226d5abb671ef8437f34ebfb365c6f59e13cee Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 18:26:15 -0700 Subject: [PATCH 76/92] fixed front matter --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index ece26025..4dfa264f 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -79,7 +79,7 @@ affiliations: index: 8 - name: Pacific Northwest National Laboratory, Richland, Washington, USA index: 9 -- name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. + - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. index: 10 - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA index: 11 #9 From 1902b70c5b776e9973075803cc7295def6e63c80 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 18:27:39 -0700 Subject: [PATCH 77/92] add PTM to "wrote manuscript" --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 4dfa264f..e4d696cc 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -216,7 +216,7 @@ Shuai Liu, Travis Jensen and Dominik Wille contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., M.S.B, J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., M.S.B, J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., P.T.M., O.B. wrote the manuscript. # References From 463ce35e99992c8214196d2e35f2a8499359c886 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 18:32:35 -0700 Subject: [PATCH 78/92] add Shuai Li to authors (but need to renumber affiliations) --- joss_paper/paper.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index e4d696cc..03791405 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -36,8 +36,10 @@ authors: orcid: 0000-0002-4221-7094 affiliation: 9 - name: Haoxi Li - orcid: 0009-0004-8369-1042 + orcid: 0009-0004-8369-1042 affiliation: 10 + - name: Shuai Li + affiliation: 17 - name: Victoria Lim orcid: 0000-0003-4030-9312 affiliation: 11 @@ -82,7 +84,7 @@ affiliations: - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. index: 10 - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA - index: 11 #9 + index: 11 - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 12 - name: PM Scientific Consulting, Basel, Switzerland @@ -93,6 +95,8 @@ affiliations: index: 15 - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA index: 16 + - name: Silicon Therapeutics LLC, Boston, USA + index: 17 date: 4 June 2024 bibliography: paper.bib From 9706f56c7cce76fb541def75ed64b103e3f8be86 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 20:18:50 -0700 Subject: [PATCH 79/92] =?UTF-8?q?Fix=20Shuai=20Liu=E2=80=99s=20name=20(sor?= =?UTF-8?q?ry)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 03791405..f49a41e9 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -38,7 +38,7 @@ authors: - name: Haoxi Li orcid: 0009-0004-8369-1042 affiliation: 10 - - name: Shuai Li + - name: Shuai Liu affiliation: 17 - name: Victoria Lim orcid: 0000-0003-4030-9312 From 3926c0c2b41bad6c4a49c452c8c3826ba0c78dbd Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 20:27:19 -0700 Subject: [PATCH 80/92] Updated author order and contributions --- joss_paper/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index f49a41e9..b04d926f 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -38,11 +38,11 @@ authors: - name: Haoxi Li orcid: 0009-0004-8369-1042 affiliation: 10 - - name: Shuai Liu - affiliation: 17 - name: Victoria Lim orcid: 0000-0003-4030-9312 affiliation: 11 + - name: Shuai Liu + affiliation: 17 - name: Domenico Marson orcid: 0000-0003-1839-9868 affiliation: 12 @@ -216,11 +216,11 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Shuai Liu, Travis Jensen and Dominik Wille contributed code to *alchemlyb*. +Travis Jensen and Dominik Wille contributed code to *alchemlyb*. # Author contributions -D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., M.S.B, J.H., T.T.J., I.M.K., H.L., H.L., V.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., P.T.M., O.B. wrote the manuscript. +D.L.D., M.R.S., D.M., and O.B. designed the project. Z.W., D.L.D., I.A., B.K.A., M.S.B, J.H., T.T.J., I.M.K., H.L., H.L., V.L., S.L., D.M., P.T.M, A.S. contributed to new features. Z.W., D.L.D., O.B. maintained the code base. Z.W., D.L.D., M.R.S, A.S., P.T.M., O.B. wrote the manuscript. # References From 9fa23522f79d5bad7acd59604d7d3ca574b90e9d Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 21:27:26 -0700 Subject: [PATCH 81/92] full author list for NAMD paper --- joss_paper/paper.bib | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 3cbd8fa1..0b3e1f82 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -132,7 +132,7 @@ @article{pham2011identifying @article{phillips2020scalable, title={Scalable molecular dynamics on CPU and GPU architectures with NAMD}, - author={Phillips, J. C. and Hardy, D. J. and Maia, J. D. C. and Stone, J. E. and Ribeiro, J. V. and Bernardi, R. C. and et al.}, + author = {Phillips,James C. and Hardy,David J. and Maia,Julio D. C. and Stone,John E. and Ribeiro,Jo{\~a}o V. and Bernardi,Rafael C. and Buch,Ronak and Fiorin,Giacomo and H{\'e}nin,J{\'e}r{\^o}me and Jiang,Wei and McGreevy,Ryan and Melo,Marcelo C. R. and Radak,Brian K. and Skeel,Robert D. and Singharoy,Abhishek and Wang,Yi and Roux,Beno{\^\i}t and Aksimentiev,Aleksei and Luthey-Schulten,Zaida and Kal{\'e},Laxmikant V. and Schulten,Klaus and Chipot,Christophe and Tajkhorshid,Emad}, journal={The Journal of Chemical Physics}, volume={153}, number={4}, From a61bddc577fddd99dce47be75fca2ca8234700b2 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 21:31:03 -0700 Subject: [PATCH 82/92] re-numbered affiliations --- joss_paper/paper.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index b04d926f..9b5c4999 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -42,26 +42,26 @@ authors: orcid: 0000-0003-4030-9312 affiliation: 11 - name: Shuai Liu - affiliation: 17 + affiliation: 12 - name: Domenico Marson orcid: 0000-0003-1839-9868 - affiliation: 12 + affiliation: 13 - name: Pascal T. Merz orcid: 0000-0002-7045-8725 - affiliation: 13 + affiliation: 14 - name: Alexander Schlaich orcid: 0000-0002-4250-363X - affiliation: 14 + affiliation: 15 - name: David Mobley orcid: 0000-0002-1083-5533 affiliation: 11 - name: Michael R. Shirts orcid: 0000-0003-3249-1097 - affiliation: 15 + affiliation: 16 - name: Oliver Beckstein orcid: 0000-0003-1340-0831 corresponding: true - affiliation: "2,16" + affiliation: "2,17" affiliations: - name: Exscientia, Oxford, UK index: 1 @@ -85,17 +85,17 @@ affiliations: index: 10 - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA index: 11 - - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy + - name: Silicon Therapeutics LLC, Boston, USA index: 12 - - name: PM Scientific Consulting, Basel, Switzerland + - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 13 - - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany + - name: PM Scientific Consulting, Basel, Switzerland index: 14 - - name: University of Colorado Boulder, Boulder, Colorado, USA + - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany index: 15 - - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA + - name: University of Colorado Boulder, Boulder, Colorado, USA index: 16 - - name: Silicon Therapeutics LLC, Boston, USA + - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA index: 17 date: 4 June 2024 From d124059787716afad255832865b20f1985a0b997 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 23:18:25 -0700 Subject: [PATCH 83/92] finalized acknowledgements - highlight contributors that are not authors on the paper: Travis Jensen (@trje3733) and Dominik Wille (@harlor) - acknowledge contributors for small fixes @helmutcarter @wehs7661 - acknowledge @VOD555 s initial contribution of code for fractional equilibration time --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 9b5c4999..5d277d90 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -216,7 +216,7 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -Travis Jensen and Dominik Wille contributed code to *alchemlyb*. +We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and We-Tse Hu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. # Author contributions From df864a7b103eb5f54bcd5bf2638c55935964fcd4 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Tue, 4 Jun 2024 23:26:58 -0700 Subject: [PATCH 84/92] clean up: removed PDF versions --- joss_paper/paper_joss_20240520.pdf | Bin 1159205 -> 0 bytes joss_paper/paper_joss_20240530.pdf | Bin 263594 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 joss_paper/paper_joss_20240520.pdf delete mode 100644 joss_paper/paper_joss_20240530.pdf diff --git a/joss_paper/paper_joss_20240520.pdf b/joss_paper/paper_joss_20240520.pdf 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ztvA}v)MQUlGZLQJdFDu?z;Oo_^+jQ#(r0Gft(TCVF!O{DRL&($N$;b>QD)J?&K*a- zuDbl@TlPj#|EQE@Y@4-%4Idxu_H$jX6gY8()Xv0c-P}{|OtTY|Wt#k6G)yjFIhM#I z=EN2h!6zzEzV`H+XEiyy7{nYUr#P$XwCy-QhqX}bNTfizW67$x2NE6cLYwL&7^JQ% z@EkOF!}v(3uydVD_@kUm9u1!*IzeKZ-BVmoDWy)_kW|YUJJr6%N*WA7&&i479uWiMdw$ From cd991d924a951af1324f08869e55c8bd28eefce3 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 5 Jun 2024 12:54:12 -0700 Subject: [PATCH 85/92] fixed name of Wei-Tse Hu in Ack --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 5d277d90..c5fe78b2 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -216,7 +216,7 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and We-Tse Hu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. +We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and Wei-Tse Hu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. # Author contributions From e85a327eb28342a2b95e0d118981770083372e95 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 5 Jun 2024 12:58:59 -0700 Subject: [PATCH 86/92] add missing DOIs - fix #376 - added DOIs that were found by https://github.com/openjournals/joss-reviews/issues/6845#issuecomment-2149351124 --- joss_paper/paper.bib | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 0b3e1f82..cfc75545 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -6,6 +6,7 @@ @article{bennett1976efficient number={2}, pages={245-268}, year={1976}, + doi={10.1016/0021-9991(76)90078-4}, } @@ -193,6 +194,7 @@ @article{zwanzig1954high number={8}, pages={1420-1426}, year={1954}, + doi={10.1063/1.1740409}, } @article{fan2020aa, @@ -286,7 +288,8 @@ @article{Thompson2022aa pages = 108171, title = {{LAMMPS} - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales}, volume = 271, - year = 2022 + year = 2022, + doi = {10.1016/j.cpc.2021.108171}, } From 4cbea4cef23063067fda5d84c5f0117abb6ee34f Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 5 Jun 2024 13:11:30 -0700 Subject: [PATCH 87/92] add DOI for Kirkwood 1935 --- joss_paper/paper.bib | 1 + 1 file changed, 1 insertion(+) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index cfc75545..9f204251 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -89,6 +89,7 @@ @article{kirkwood1935statistical number={5}, pages={300-313}, year={1935}, + doi={10.1063/1.1749657}, } @article{klimovich2015guidelines, From 4f01ac830851f7e22fafa3e298be423476bee0c5 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Wed, 5 Jun 2024 19:55:08 -0700 Subject: [PATCH 88/92] Fix name Hu -> Hsu in Ack Co-authored-by: Pascal Merz --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index c5fe78b2..964bcb08 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -216,7 +216,7 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and Wei-Tse Hu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. +We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and Wei-Tse Hsu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. # Author contributions From 7a2b140aa0301e48abf7af0b702b87c09ea7aed2 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Wed, 26 Jun 2024 15:59:06 +0100 Subject: [PATCH 89/92] Update affiliation --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 964bcb08..30c5af93 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -63,7 +63,7 @@ authors: corresponding: true affiliation: "2,17" affiliations: - - name: Exscientia, Oxford, UK + - name: Exscientia plc, Schroedinger Building, Oxford, United Kingdom index: 1 - name: Department of Physics, Arizona State University, Tempe, Arizona, USA index: 2 From 1f32e8801a178f502203d20f13427278aa1dd7b0 Mon Sep 17 00:00:00 2001 From: Zhiyi Wu Date: Sat, 17 Aug 2024 21:18:17 +0100 Subject: [PATCH 90/92] Minor changes to the paper (#387) * update * remove doi * add orcid --- joss_paper/paper.bib | 20 +++++++++++--------- joss_paper/paper.md | 15 ++++++++------- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/joss_paper/paper.bib b/joss_paper/paper.bib index 9f204251..75b74b6f 100644 --- a/joss_paper/paper.bib +++ b/joss_paper/paper.bib @@ -12,7 +12,7 @@ @article{bennett1976efficient @article{Case2005uq, author = {Case, David A and Cheatham, 3rd, Thomas E and Darden, Tom and Gohlke, Holger and Luo, Ray and Merz, Jr, Kenneth M and Onufriev, Alexey and Simmerling, Carlos and Wang, Bing and Woods, Robert J}, - journal = {J Comput Chem}, + journal = {Journal of Computational Chemistry}, number = {16}, pages = {1668-1688}, title = {The {Amber} biomolecular simulation programs}, @@ -25,7 +25,7 @@ @article{Case2005uq @article{chodera2007use, title={Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations}, author={J. D. Chodera and Swope, W. C. and Pitera, J. W. and Seok, C. and Dill, K. A.}, - journal={J Chem Theory Comput}, + journal={Journal of Chemical Theory and Computation}, volume={3}, number={1}, pages={26-41}, @@ -48,7 +48,7 @@ @article{Nejahi2021aa @article{deng2009computations, title={Computations of standard binding free energies with molecular dynamics simulations}, author={Deng, Y. and Roux, B.}, - journal={J Phys Chem B}, + journal={The Journal of Physical Chemistry B}, volume={113}, number={8}, pages={2234-2246}, @@ -95,7 +95,7 @@ @article{kirkwood1935statistical @article{klimovich2015guidelines, title={Guidelines for the analysis of free energy calculations}, author={Klimovich, P. V. and Shirts, M. R. and Mobley, D. L.}, - journal={J Comput Aided Mol Des}, + journal={Journal of Computer-Aided Molecular Design}, volume={29}, number={5}, pages={397-411}, @@ -108,12 +108,13 @@ @book{merz2010drug author={Merz, Jr, K. M. and Ringe, D. and Reynolds, C. H.}, year={2010}, publisher={Cambridge University Press}, + doi = {10.48550/arXiv.1309.0238}, } @article{paliwal2011benchmark, title={A Benchmark Test Set for Alchemical Free Energy Transformations and Its Use to Quantify Error in Common Free Energy Methods}, author={Paliwal, H. and Shirts, M. R.}, - journal={J Chem Theory Comput}, + journal={Journal of Chemical Theory and Computation}, volume={7}, number={12}, pages={4115-4134}, @@ -124,7 +125,7 @@ @article{paliwal2011benchmark @article{pham2011identifying, title={Identifying low variance pathways for free energy calculations of molecular transformations in solution phase}, author={Pham, T. T. and Shirts, M. R.}, - journal={J Chem Phys}, + journal={The Journal of Chemical Physics}, volume={135}, number={3}, pages={034114}, @@ -146,7 +147,7 @@ @article{phillips2020scalable @article{pohorille2010good, title={Good practices in free-energy calculations}, author={Pohorille, A. and Jarzynski, C. and Chipot, C.}, - journal={J Phys Chem B}, + journal={The Journal of Physical Chemistry B}, volume={114}, number={32}, pages={10235-10253}, @@ -168,7 +169,7 @@ @article{Abraham2015aa @article{shirts2008statistically, title={Statistically optimal analysis of samples from multiple equilibrium states}, author={Shirts, M. R. and Chodera, J. D.}, - journal={J Chem Phys}, + journal={The Journal of Chemical Physics}, volume={129}, number={12}, pages={124105}, @@ -179,7 +180,7 @@ @article{shirts2008statistically @article{yang2004free, title={Free energy simulations: use of reverse cumulative averaging to determine the equilibrated region and the time required for convergence}, author={Yang, W. and Bitetti-Putzer, R. and Karplus, M.}, - journal={J Chem Phys}, + journal={The Journal of Chemical Physics}, volume={120}, number={6}, pages={2618-2628}, @@ -267,6 +268,7 @@ @inproceedings{sklearn2013api booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning}, year = {2013}, pages = {108--122}, + doi = {10.48550/arXiv.1309.0238}, } diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 30c5af93..7b8a6541 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -42,6 +42,7 @@ authors: orcid: 0000-0003-4030-9312 affiliation: 11 - name: Shuai Liu + orcid: 0000-0002-8632-633X affiliation: 12 - name: Domenico Marson orcid: 0000-0003-1839-9868 @@ -69,7 +70,7 @@ affiliations: index: 2 - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) index: 3 - - name: Open Free Energy, Open Molecular Software Foundation, Davis, 95616 California, United States + - name: Open Free Energy, Open Molecular Software Foundation, Davis, California, United States index: 4 - name: Differentiated Therapeutics, San Diego, CA index: 5 @@ -91,7 +92,7 @@ affiliations: index: 13 - name: PM Scientific Consulting, Basel, Switzerland index: 14 - - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, 70569 Stuttgart, Germany + - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, Stuttgart, Germany index: 15 - name: University of Colorado Boulder, Boulder, Colorado, USA index: 16 @@ -105,7 +106,7 @@ bibliography: paper.bib # Summary -*alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an important method in computational chemistry and biology, most notably in the field of drug discovery. +*alchemlyb* is an open-source Python software package for the analysis of alchemical free energy calculations, an important method in computational chemistry and biology, most notably in the field of drug discovery [@merz2010drug]. Its functionality contains individual composable building blocks for all aspects of a full typical free energy analysis workflow, starting with the extraction of raw data from the output of diverse molecular simulation packages, moving on to data preprocessing tasks such as decorrelation of time series, using various estimators to derive free energy estimates from simulation samples, and finally providing quality analysis tools for data convergence checking and visualization. *alchemlyb* also contains high-level end-to-end workflows that combine multiple building blocks into a user-friendly analysis pipeline from the initial data input stage to the final results. This workflow functionality enhances accessibility by enabling researchers from diverse scientific backgrounds, and not solely computational chemistry specialists, to use *alchemlyb* effectively. @@ -140,7 +141,7 @@ Notably, *alchemlyb*'s robust and user-friendly nature has led to its integratio # Background: Alchemical free energy calculations -Free energy differences are fundamental to understand many different processes at the molecular scale, ranging from the binding of drug molecules to their receptor proteins or nucleic acids through the partitioning of molecules into different solvents or phases to the stability of crystals and biomolecules. +Free energy differences are fundamental to understand many different processes at the molecular scale, ranging from the binding of drug molecules to their receptor proteins or nucleic acids through the partitioning of molecules into different solvents or phases to the stability of crystals and biomolecules [@deng2009computations]. The calculation of such transfer free energies involves constructing two end states where a target molecule interacts with different environments. For example, in a solvation free energy calculation, in one state (the coupled state) the target molecule interacts with a solvent (in the case of hydration free energies, water), while in the other state (the decoupled state) the ligand has no intermolecular interactions, which mimics the transfer of a ligand from infinite dilution in the solvent to the gas phase. The solvation free energy is then obtained by calculating the free energy difference between these two end states, but it is crucial to ensure sufficient overlap in phase space between the coupled and decoupled states, a condition often challenging to achieve. @@ -155,7 +156,7 @@ Estimators are then applied to these data to compute free energy differences bet # Implementation ## Core design principles -*alchemlyb* is a Python library that seeks to make doing alchemical free energy calculations easier and less error prone. +*alchemlyb* is a Python library that seeks to make alchemical free energy calculations easier and less error prone. It includes functionality for parsing data from file formats of widely used simulation packages, subsampling these data, and fitting these data with an estimator to obtain free energies. Functions are simple in usage and pure in scope, and can be chained together to build customized analyses of data while estimators are implemented as classes that follow the tried-and-tested scikit-learn API [@sklearn2013api]. General and robust workflows following best practices are also provided, which can be used as reference implementations and examples. @@ -192,7 +193,7 @@ FEP category estimators (module `alchemlyb.estimators.FEP`) include Bennett Acce To evaluate the accuracy of the free energy estimate, *alchemlyb* offers a range of assessment tools. The error of the TI method is correlated with the average curvature [@pham2011identifying], while the error of FEP estimators depends on the overlap in sampled energy distributions [@pohorille2010good]. *alchemlyb* creates visualizations of the smoothness of the integrand for TI estimators and the overlap matrix for FEP estimators, which can be qualitatively and quantitatively analyzed to determine the degree of overlap between simulated alchemical states, and suggest whether additional simulations should be run. -For statistical validity, the accumulated samples should be collected from equilibrated simulations and *alchemlyb* contains tools for assessing (`alchemlyb.convergence`) and plotting (`alchemlyb.visualisation`) the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially un-equilibrated data. +For statistical validity, the accumulated samples should be collected from equilibrated simulations and *alchemlyb* contains tools for assessing (`alchemlyb.convergence`) and plotting (`alchemlyb.visualisation`) the convergence of the free energy estimate as a function of simulation time [@yang2004free] and means to compute the "fractional equilibration time" [@fan2020aa] to detect potentially non-equilibrated data. *alchemlyb* offers all these tools as a library for users to customize each stage of the analysis (\autoref{fig:buildingblocks}). @@ -211,7 +212,7 @@ It can directly estimate quantities such as solvation free energies and makes it # Acknowledgements -Some work on alchemlyb was supported by grants from the National +Some work on *alchemlyb* was supported by grants from the National Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. From 4f7e2dbf024a2d552ced51acbe0fd4153ea1e803 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Thu, 19 Sep 2024 14:03:43 -0700 Subject: [PATCH 91/92] add @jaclark5 to acknowledgements - acknowledging recent additions to 2.4.0 and 2.4.1 release (after the review of the manuscript) - approved in https://github.com/alchemistry/alchemlyb/pull/328#discussion_r1767590941 --- joss_paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index 7b8a6541..ef09d590 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -217,7 +217,7 @@ Institutes of Health (Award No R01GM118772 to O.B., R35GM148236 to D.M., K08GM139031 to T.T.J.) and the National Science Foundation (award ACI-1443054 to O.B.). A.S. acknowledges funding by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 – 390740016 and support by the Stuttgart Center for Simulation Science (SimTech). The sponsors were not involved in any aspects of the research or the writing of the manuscript. -We thank Dominik Wille and Travis Jensen for substantial code contributions, Helmut Carter and Wei-Tse Hsu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. +We thank Dominik Wille, Travis Jensen, and Jennifer A. Clark for substantial code contributions, Helmut Carter and Wei-Tse Hsu for small fixes, Shujie Fan for initial code for fractional equilibration time calculation, and Jan Janssen for creating the initial conda-forge package. # Author contributions From 27caba89c22801ae275d87bf38ab122aa34d45e3 Mon Sep 17 00:00:00 2001 From: Oliver Beckstein Date: Mon, 23 Sep 2024 17:42:07 -0700 Subject: [PATCH 92/92] paper affiliations: spell out USA address https://github.com/openjournals/joss-reviews/issues/6934#issuecomment-2367669802 --- joss_paper/paper.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/joss_paper/paper.md b/joss_paper/paper.md index ef09d590..b58591eb 100644 --- a/joss_paper/paper.md +++ b/joss_paper/paper.md @@ -66,27 +66,27 @@ authors: affiliations: - name: Exscientia plc, Schroedinger Building, Oxford, United Kingdom index: 1 - - name: Department of Physics, Arizona State University, Tempe, Arizona, USA + - name: Department of Physics, Arizona State University, Tempe, Arizona, United States of America index: 2 - - name: Datryllic LLC, Phoenix, Arizona, USA (present affiliation) + - name: Datryllic LLC, Phoenix, Arizona, United States of America (present affiliation) index: 3 - name: Open Free Energy, Open Molecular Software Foundation, Davis, California, United States index: 4 - name: Differentiated Therapeutics, San Diego, CA index: 5 - - name: Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan, USA + - name: Department of Chemical Engineering and Materials Science, Wayne State University, Detroit, Michigan, United States of America index: 6 - name: Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris, France index: 7 - - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA + - name: Department of Anesthesiology and Critical Care, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America index: 8 - - name: Pacific Northwest National Laboratory, Richland, Washington, USA + - name: Pacific Northwest National Laboratory, Richland, Washington, United States of America index: 9 - - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA. + - name: UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States of America index: 10 - - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, USA + - name: Departments of Pharmaceutical Sciences and Chemistry, University of California Irvine, Irvine, California, United States of America index: 11 - - name: Silicon Therapeutics LLC, Boston, USA + - name: Silicon Therapeutics LLC, Boston, United States of America index: 12 - name: Molecular Biology and Nanotechnology Laboratory (MolBNL@UniTS), DEA, University of Trieste, Trieste, Italy index: 13 @@ -94,9 +94,9 @@ affiliations: index: 14 - name: Stuttgart Center for Simulation Science (SC SimTech) & Institute for Computational Physics, University of Stuttgart, Stuttgart, Germany index: 15 - - name: University of Colorado Boulder, Boulder, Colorado, USA + - name: University of Colorado Boulder, Boulder, Colorado, United States of America index: 16 - - name: Center for Biological Physics, Arizona State University, Tempe, AZ, USA + - name: Center for Biological Physics, Arizona State University, Tempe, AZ, United States of America index: 17 date: 4 June 2024

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