QC-GN²OMS² - A Graph Neural Net for ms/ms prediction.

Prediction:

• Generate graph feature database in a pickle file

  • top_db.py : creates TOP feature database using RDKit
  • qc1_3d.py : generates 3D molecular structures for the QC1 database
  • qc1_db.py : aggregates the 3D structures and calculates features
  • qc2_db.py : calculates BDE features for the QC2 model and stores them in a pickle file

• Run the prediction script with the associated model and database

  • pred_afb.py : predict a spectra using the QC2 model

• Install:

git clone https://github.com/PNNL-m-q/qcgnoms.git
cd qcgnoms
mamba env create -f qcgnoms.yml
conda activate qcgnoms
pip install .

• Example:

$ python
>>> from qcgnoms import predict_msms
mz, itn = predict_msms("CC(C)/C=C/CCCCC(=O)NCC1=CC(=C(C=C1)O)OC", 45)

Training:

Requires a MS/MS database in a pickle file. Required data columns:

1. Collision Energy in eV
2. InChI
3. Smiles
4. M/Z: numpy array of high resolution m/z values.
5. Intensity: numpy array of MS intensities.

• See data/msms_sample.pkl

• Generate graph feature database in a pickle file

  • top_db.py : creates TOP feature database using RDKit
  • qc1_3d.py : generates 3D molecular structures for the QC1 database
  • qc1_db.py : aggregates the 3D structures and calculates features
  • qc2_db.py : calculates BDE features for the QC2 model and stores them in a pickle file

• Test datasets are assigned by first training the control model with train_control.py

• Test set data are located in test_set/

Dependencies

• torch
• torch-geometric
• alfabet https://github.com/NREL/alfabet
• xtb https://github.com/grimme-lab/xtb
• openbabel
• pandas
• matplotlib
• numpy

Citation

QC-GN²oMS²: a Graph Neural Net for High Resolution Mass Spectra Prediction
Richard Overstreet, Ethan King, Julia Nguyen, Danielle Ciesielski bioRxiv 2023.01.16.524269; doi: https://doi.org/10.1101/2023.01.16.524269