(Under Construction and Subject to Change)
This is the official PyTorch implementation for GraphRetro (Somnath et al. 2021), a graph based model for one-step retrosynthesis prediction. Our model achieves the transformation from products to reactants using a two stage decomposition:
a) Edit Prediction: Identifies edits given a product molecule, which upon application give intermediate molecules called synthons
b) Synthon Completion: Completes synthons into reactants by adding subgraphs called leaving groups from a precomputed vocabulary.
This assumes conda is installed on your system
If conda is not installed, download the Miniconda installer.
If conda is installed, run the following commands:
echo 'export SEQ_GRAPH_RETRO=/path/to/dir/' >> ~/.bashrc
source ~/.bashrc
conda env create -f environment.yml
source activate seq_gr
python setup.py develop(or install)
The original and canonicalized files are provided under datasets/uspto-50k/. Please make sure to move them to $SEQ_GRAPH_RETRO/ before use.
Before preparing inputs, we canonicalize the products. This can be done by running,
python data_process/canonicalize_prod.py --filename train.csv
python data_process/canonicalize_prod.py --filename eval.csv
python data_process/canonicalize_prod.py --filename test.csv
This step can also be skipped if the canonicalized files are already present. The preprocessing steps now directly work with the canonicalized files.
python data_process/parse_info.py --mode train
python data_process/parse_info.py --mode eval
python data_process/parse_info.py --mode test
python data_process/core_edits/bond_edits.py
python data_process/lg_edits/lg_classifier.py
python data_process/lg_edits/lg_tensors.py
Trained models are stored in experiments/. You can override this by adjusting --exp_dir before training.
Model configurations are stored in config/MODEL_NAME
where MODEL_NAME is one of {single_edit, lg_ind}.
To run a model,
python scripts/benchmarks/run_model.py --config_file configs/MODEL_NAME/defaults.yaml
NOTE: We recently updated the code to use wandb for experiment tracking. You would need to setup wandb before being able to train a model.
To evaluate the trained model, run
python scripts/eval/single_edit_lg.py --edits_exp EDITS_EXP --edits_step EDITS_STEP \
--lg_exp LG_EXP --lg_step LG_STEP
This will setup a model with the edit prediction module loaded from experiment EDITS_EXP and checkpoint EDITS_STEP
and the synthon completion module loaded from experiment LG_EXP and checkpoint LG_STEP.
To reproduce our results, please run the command,
./eval.sh
This will display the results for reaction class unknown and known setting.
This project is licensed under the MIT-License. Please see LICENSE.md for more details.
If you find our code useful for your work, please cite our paper:
@inproceedings{
somnath2021learning,
title={Learning Graph Models for Retrosynthesis Prediction},
author={Vignesh Ram Somnath and Charlotte Bunne and Connor W. Coley and Andreas Krause and Regina Barzilay},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=SnONpXZ_uQ_}
}
If you have any questions about the code, or want to report a bug, please raise a GitHub issue.
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