Original work is at https://arxiv.org/pdf/2002.03230.pdf
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
- 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 seedata/qed/test.txt.
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:
- Extract substructure vocabulary from a given set of molecules:
python get_vocab.py --ncpu 16 < data/chembl/all.txt > vocab.txt
- 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/
- 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
- Sample molecules from a model checkpoint
python generate.py --vocab data/chembl/vocab.txt --model ckpt/chembl-pretrained/model.ckpt --nsamples 1000