This is the complete code for the OTGK_DP framework to predict ADMET durg properties. The work is pubished in the following papers:
(Paper)
@INPROCEEDINGS{10504311,
author={Aburidi, Mohammed and Marcia, Roummel},
booktitle={2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)},
title={Wasserstein Distance-Based Graph Kernel for Enhancing Drug Safety and Efficacy Prediction *},
year={2024},
volume={},
number={},
pages={113-119},
keywords={Drugs;Proteins;Adaptation models;Toxicology;Pharmacodynamics;Predictive models;Safety;Optimal Transport;Wasserstein Distance;Graph Matching;Drug Discovery;ADMET Properties},
doi={10.1109/AIMHC59811.2024.00029}}
Comments/Bugs/Problems: maburidi@ucmerced.edu maburidi@gmail.com
December, 2023. Initial release
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Find the Colab tutorial in the tutorial folder to run this repository and to predict the drug properties.
The data used in this project is Therapeutics Data Commons - TDC)
To list all of the datasets in AMDE, run the following
from tdc import utils
utils.retrieve_dataset_names('ADME')
To list all of the datasets in TOX, run the following
from tdc import utils
utils.retrieve_dataset_names('TOX')
Make sure to install the following dependincies:
PyTDC
rdkit
torch_geometric
POT
py3Dmol
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Cite as:
@inproceedings{aburidi2024_1,
author = {M. Aburidi and R. Marica},
journal = {Scientific Reports},
title = {Optimal Transport-Based Graph Kernels for Drug Property Prediction},
url = {},
year = {2024},
}
@INPROCEEDINGS{10504311,
author={Aburidi, Mohammed and Marcia, Roummel},
booktitle={2024 IEEE First International Conference on Artificial Intelligence for Medicine, Health and Care (AIMHC)},
title={Wasserstein Distance-Based Graph Kernel for Enhancing Drug Safety and Efficacy Prediction},
year={2024},
volume={},
number={},
pages={113-119},
keywords={Drugs;Proteins;Adaptation models;Toxicology;Pharmacodynamics;Predictive models;Safety;Optimal Transport;Wasserstein Distance;Graph Matching;Drug Discovery;ADMET Properties},
doi={10.1109/AIMHC59811.2024.00029}}