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 >= 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

Property-guided molcule generation procedure (a.k.a. finetuning)

The following script loads a trained Chemprop model and finetunes a pre-trained molecule language model to generate molecules with specific chemical properties.

mkdir ckpt/finetune
python finetune_generator.py --train ${ACTIVE_MOLECULES} --vocab data/chembl/vocab.txt --generative_model ckpt/chembl-pretrained/model.ckpt --chemprop_model ${YOUR_PROPERTY_PREDICTOR} --min_similarity 0.1 --max_similarity 0.5 --nsample 10000 --epoch 10 --threshold 0.5 --save_dir ckpt/finetune

Here ${ACTIVE_MOLECULES} should contain a list of experimentally verified active molecules.

${YOUR_PROPERTY_PREDICTOR} should be a directory containing saved chemprop model checkpoint.

--max_similarity 0.5 means any novel molecule should have nearest neighbor similarity lower than 0.5 to any known active molecules in ${ACTIVE_MOLECULES}` file.

--nsample 10000 means to sample 10000 molecules in each epoch.

--threshold 0.5 is the activity threshold. A molecule is considered as active if its predicted chemprop score is greater than 0.5.

In each epoch, generated active molecules are saved in ckpt/finetune/good_molecules.${epoch}. All the novel active molecules are saved in ckpt/finetune/new_molecules.${epoch}

Molecule translation training procedure

Molecule translation is often useful for lead optimization (i.e., modifying a given molecule to improve its properties)

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

python get_vocab.py --ncpu 16 < data/qed/mols.txt > vocab.txt

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

2. Preprocess training data:

python preprocess.py --train data/qed/train_pairs.txt --vocab data/qed/vocab.txt --ncpu 16
mkdir train_processed
mv tensor* train_processed/

3. Train the model:

mkdir ckpt/translation
python train_translator.py --train train_processed/ --vocab data/qed/vocab.txt --save_dir ckpt/translation

4. Make prediction on your lead compounds (you can use any model checkpoint, here we use model.5 for illustration)

python translate.py --test data/qed/valid.txt --vocab data/qed/vocab.txt --model ckpt/translation/model.5 --num_decode 20 > results.csv

Polymer generation

The polymer generation code is in the polymer/ folder. The polymer generation code is similar to train_generator.py, but the substructures are tailored for polymers. For generating regular drug like molecules, we recommend to use train_generator.py in the root directory.