/hgraph2graph

Hierarchical Graph-to-Graph Translation for Molecules

Primary LanguagePythonMIT LicenseMIT

Hierarchical Generation of Molecular Graphs using Structural Motifs

Our paper is at https://arxiv.org/pdf/2002.03230.pdf

Installation

First install the dependencies via conda:

  • PyTorch >= 1.0.0
  • networkx
  • RDKit
  • numpy
  • Python >= 3.6

And then run pip install .

Polymer Generation

The polymer generation code is in the polymers/ folder.

Graph translation Data Format

  • The training file should contain pairs of molecules (molA, molB) that are similar to each other but molB has better chemical properties. Please see data/qed/train_pairs.txt.
  • The test file is a list of molecules to be optimized. Please see data/qed/test.txt.

Graph translation training procedure

  1. Extract substructure vocabulary from a given set of molecules:
python get_vocab.py < data/qed/mols.txt > vocab.txt

Please replace data/qed/mols.txt with your molecules data file.

  1. Preprocess training data:
python preprocess.py --train data/qed/train_pairs.txt --vocab data/qed/vocab.txt --ncpu 16 < data/qed/train_pairs.txt
mkdir train_processed
mv tensor* train_processed/

Please replace --train and --vocab with training and vocab file.

  1. Train the model:
mkdir models/
python gnn_train.py --train train_processed/ --vocab data/qed/vocab.txt --save_dir models/
  1. Make prediction on your lead compounds (you can use any model checkpoint, here we use model.5 for illustration)
python decode.py --test data/qed/valid.txt --vocab data/qed/vocab.txt --model models/model.5 --num_decode 20 > results.csv