https://github.com/merck/molprop
fuses molecular language and graph representation for property prediction
https://github.com/merck/molprop
Last synced: 7 months ago
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fuses molecular language and graph representation for property prediction
- Host: GitHub
- URL: https://github.com/merck/molprop
- Owner: Merck
- Created: 2024-02-13T22:03:47.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-01T00:14:18.000Z (almost 2 years ago)
- Last Synced: 2025-08-31T18:05:26.821Z (8 months ago)
- Language: Python
- Homepage:
- Size: 678 KB
- Stars: 19
- Watchers: 6
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE/gnu-gpl-v3.0.md
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README
# MolPROP: Molecular Property Predictions with Multimodal Language and Graph Fusion
fuses molecular language and graph representation for property prediction
current models include:
- language -- ChemBERTa-2-77M-MLM, ChemBERTa-2-77M-MTR
- graph -- GCN, GAT, GATv2
```
@article{rollins2024,
title = {{MolPROP}: {Molecular} {Property} {Prediction} with {Multimodal} {Language} and {Graph} {Fusion}},
journal = {Journal of Cheminformatics},
author = {Rollins, Zachary A and Cheng, Alan C and Metwally, Essam},
month = may,
year = {2024}}
```
## requirements
- git lfs (for locally stored language models)
- install git lfs for your machine: https://github.com/git-lfs/git-lfs
- chemberta requires local installation to config: https://huggingface.co/DeepChem
- molprop.yaml
```
conda env create --name molprop --file molprop.yaml
```
## preprocess data
### data splitting
- data splits to reproduce manuscript results are provided: ./data/*csv
- data should be in data directory (e.g., csv format) and organized such that SMILES string and endpoints are in separate columns
- data can be split using the Bemis-Murko scaffold split implemented by DeepChem
- explore the help options to execute the data split procedure
```
python ./data/split.py --help
```
## train and hyperparameter tune
- training and tuning execution is specified by the configuration files (config/setup.json)
```
python ./src/train_tune.py
```
### hyperparameter tune
- setup["training"]["ray_tune"] == True
- specify hyperparameter search space in the '__main__' of ./src/train_tune.py
- ray cluster must be initialized before hyperparameter tuning execution
- submit PBS script with specified num_cpus and num_gpus
- start ray cluster
```
ray start --head --num-cpus=8 --num-gpus=4 --temp-dir="/absolute/path/to/temporary/storage/"
python ./src/train_tune.py
```
### inference and holdout
- test trained/validated models on holdout by specifying config/setup.json
- MolPROP models trained on data in manuscript are located in models/weights
- setup['holdout']['model_path']
- setup['holdout']['holdout_data_path]
```
python ./src/holdout.py
```
### logging information and model storage
- train_tune.log files are recorded and saved for every time stamped batch run
- runs are also recorded on tensorboard
- ***** = unqiue file identifier (e.g., time stamp or number)
```
logs/batch_*****/train_tune.log
logs/batch_*****/events.out.tfevents.***** (setup["training"]["ray_tune"] == False)
logs/batch_*****/ray_tune/hp_tune_*****/checkpoint_*****/events.out.tfevents.***** (setup["training"]["ray_tune"] == True)
```
- hyperparameter tune runs are implemented by ray tune and models are stored
- non-hyperparameter tuned models are also stored
```
logs/batch_*****/ray_tune/hp_tune_*****/checkpoint_*****/dict_checkpoint.pkl (setup["training"]["ray_tune"] == True)
models/weights/batch_*****/MOLPROP*****.pth (setup["training"]["ray_tune"] == False)
```
# License
MolPROP fuses molecular language and graph for property prediction.
Copyright © 2023 Merck & Co., Inc., Rahway, NJ, USA and its affiliates. All rights reserved.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .