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Molecular Graph Generation by Decomposition and Reassembling

This repository is for MOLDR: Molecular Graph Generation by Decomposition and Reassembling.

Installation

git clone https://github.com/Masatsugar/graph-decomposition-reassembling.git
conda install create --file env.yaml
cd graph-decomposition-reassembling/mi-collections
python setup.py install mi-collections

Usage

Set python path: export PYTHONPATH=..

Decomposition

In decomposition step, molecules are decomposed into subgraphs by gSpan. You can select raw or junction tree data.

from pathlib import Path
from mi_collections.moldr.decompose import MolsMining, DefaultConfig
from mi_collections.chemutils import get_mol

test_smiles = [
    "CC1CCC2=CC=CC=C2O1",
    "CC",
    "COC",
    "c1ccccc1",
    "CC1C(=O)NC(=O)S1",
    "CO",
]
mols = [get_mol(s) for s in test_smiles]
minsup = int(len(mols) * 0.1)
config = DefaultConfig(
    data_path=Path("zinc_jt.data"), support=minsup, lower=2, upper=7, method="jt"
)
runner = MolsMining(config)
gspan = runner.decompose(mols)

If you want to see the subgraphs in detail, see examples/decomponsition.ipynb.

Reassembing

• Training The agent takes an action from building blocks obtained from a decomposition step. The agent is trained by PPO with RLlib.

python train.py --epochs 100 --num_workers 40 --num_gpus 1

Generated molecules are sampled through the trained agent. Select and rewrite the model path that you want to use.

python run_moldr.py 

Citation

@article{Yamada2023MOLDR,
  author={Yamada, Masatsugu and Sugiyama, Mahito},
  title={Molecular Graph Generation by Decomposition and Reassembling},
  journal={ACS Omega},
  year={2023},
  month={Jun},
  day={06},
  publisher={American Chemical Society},
  volume={8},
  number={22},
  pages={19575-19586},
  doi={10.1021/acsomega.3c01078},
  url={https://doi.org/10.1021/acsomega.3c01078}
}