GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
The paper is available on arXiv, https://arxiv.org/abs/1905.11600.
Citation
If you find our work useful in your research, please consider citing:
@article{kaushalya2019graphnvp,
title={GraphNVP: An Invertible Flow Model for Generating Molecular Graphs},
author={Kaushalya, Madhawa and Katushiko, Ishiguro and Kosuke, Nakago and Motoki, Abe},
journal={arXiv preprint arXiv:1905.11600},
year={2019}
}
Dependencies
- Python 3.6+
- Chainer<=5.2.0 (Note: code may not work with chainer>=6.0.0)
- cupy<=5.2.0 (Note: please install the same version with chainer)
- chainer-chemistry==0.5.0
- rdkit (release 2017.09.3.0) [Check chainer-chemistry for more information]
- CUDA-Aware MPI (Only for running on multiple GPUS using ChainerMN. Check ChainerMN installation guide for more information.)
Example instllation
conda install -c rdkit rdkit==2017.09.3.0
pip install -r requirements.txt
# please modify XX into your system's CUDA version
# pip install cupy-cudaXX==5.2.0
pip install cupy-cuda100==5.2.0
# When you want to use ChainerMN (Multi-GPU training)
pip install mpi4py
Tested datasets
- QM 9
- Zinc 250k
Pre-trained models
Pre-trained model files are uploaded. Please download and place them to models directory.
How to run code
Dataset preparation
cd data
# Download and preprocess QM9 dataset
python download_data.py --data_name=qm9
# Download and preprocess ZINC-250k dataset
python download_data.py --data_name=zinc250kWe use the same train / validation split used by Kusner et al. (Grammar VAE)
Training
- QM9
python train_model.py -f qm9_relgcn_kekulized_ggnp.npz -b 256 -x 200 --gpu 0 --num_node_masks 9 --num_channel_masks 9 \
--num_node_coupling 36 --num_channel_coupling 27 --num_atom_types 4 --apply_batch_norm True --node_mask_size 15 \
--debug True --additive_transformations --save_dir=results/qm9 --learn_dist yes- Zinc250k
python train_model.py -f zinc250k_relgcn_kekulized_ggnp.npz --data_name=zinc250k --num_atoms=38 -b 128 -x 200 --gpu 0 \
--num_node_masks 38 --num_channel_masks 38 --num_node_coupling 38 --num_channel_coupling 38 --num_atom_types 9 \
--apply_batch_norm True --node_mask_size 15 --debug True --additive_transformations \
--save_dir=results/zinc250k --learn_dist yesFor multi-GPU training please check scripts/train_qm9_chainermn.sh and scripts/train_zinc250k_chainermn.sh.
Evaluation (Generating molecules with a trained model)
A pre-trained model along with hyperparameters is available. Please refer "Pre-trained models" section.
- QM9
Executing the bash script generate.sh will generate molecules.
python generate.py -snapshot graph-nvp-final.npz \
--gpu -1 \
--data_name qm9 \
--data_dir data \
--hyperparams-path graphnvp-params.json \
--batch-size 1000 \
--model_dir models/qm9 \
--temperature 0.8 \
--delta 0.05 \
--n_experiments 1- Zinc250k
python generate.py -snapshot graph-nvp-final-new.npz \
--gpu -1 \
--data_name zinc250k \
--data_dir data \
--hyperparams-path graph-nvp-new-params.json \
--batch-size 1000 \
--model_dir models/zinc-250k \
--temperature 0.75 \
--delta 0.05 \
--n_experiments 1 \
--molecule_file zinc250k_relgcn_kekulized_ggnp.npzProperty optimization
- QM9 example
python optimize_property.py -snapshot graph-nvp-final.npz \
--hyperparams_path graphnvp-params.json \
--batch_size 1000 \
--model_dir models/qm9 \
--data_dir data \
--molecule_file qm9_relgcn_kekulized_ggnp.npz \
--temperature 1.0 \
--delta 0.5 \
--img_format png \
--property_name qed \
--property_model qed_model.pkl