Hierarchical Generation and Prediction of Molecular Graphs using Structural Motifs

Original work is at https://arxiv.org/pdf/2002.03230.pdf

Installation

First install the dependencies via conda (ensure that you generate vocab and preprocess with the same python environment as you train and sample!):

• PyTorch >= 1.0.0
• networkx
• RDKit >= 2019.03
• numpy
• Python >= 3.6

And then run pip install .. Additional dependency for property-guided finetuning:

• Chemprop >= 1.2.0

Data Format

• For graph generation, each line of a training file is a SMILES string of a molecule
• For graph translation, each line of a training file is a pair 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.

Molecule generation pretraining procedure

We can train a molecular language model on a large corpus of unlabeled molecules. We have uploaded a model checkpoint pre-trained on ChEMBL dataset in ckpt/chembl-pretrained/model.ckpt. If you wish to train your own language model, please follow the steps below:

1. Extract substructure vocabulary from a given set of molecules:

python get_vocab.py --ncpu 16 < data/chembl/all.txt > vocab.txt

2. Preprocess training data:

python preprocess.py --train data/chembl/all.txt --vocab data/chembl/all.txt --ncpu 16 --mode single
mkdir train_processed
mv tensor* train_processed/

3. Train graph generation model

mkdir ckpt/chembl-pretrained
python train_generator.py --train train_processed/ --vocab data/chembl/vocab.txt --save_dir ckpt/chembl-pretrained

4. Sample molecules from a model checkpoint

python generate.py --vocab data/chembl/vocab.txt --model ckpt/chembl-pretrained/model.ckpt --nsamples 1000