PyTorch Implementation of MolGAN

Usage • Exhibition • Citation • Credits • Licence

Overview

This repository contains a PyTorch implementation of MolGAN: An implicit generative model for small molecular graphs (https://arxiv.org/abs/1805.11973).

Usage

You can either run MolGAN or MolVAE. Please download and preprocess the dataset before training molGAN.

Data Download

Just simply run a bash script. You can find such a bash script in the data directory. If you see warnings or even errors, please just don't worry.

If you wish to use the QM9 dataset, you can skip the data downloading and directly proceed to data preprocessing.

bash download_dataset.sh

Data Preprocessing

Just run the python script below. You will need to comment different different code sections in the main function. It is too easy to figure out how to do.

python sparse_molecular_dataset.py

MolGAN

Simply run the following command to train.

python main_gan.py

MolVAE

For your convenience, a VAE version is also implemented.

python main_vae.py

Testing

You will need to change some arguments in args.py to test the saved model. It is too easy and I believe you can figure out how to do it.

Kind Reminder

If you witness low or even zero validity for generated molecules during training, that is normal. Please just don't worry.

Molecule Generation Exhibition

These results are trained with the QM9 dataset.

Pure RL (WGAN-Lambda = 0.0)

Mixture of RL and GAN (WGAN-Lambda = 0.5)

Pure GAN (WGAN-Lambda = 1.0)

MolVAE Reconstruction

MolVAE Sampling

Dependencies

I use PyTorch 1.5. There is no magic for installing packages. You can just install all the required packages if you run into no-such-package issues :-)

Contacts

If you have any questions regarding this implementation. Please lodge Github issues. You can also contact Zhenyue Qin ([email protected]). I aim to respond emails as soon as I see them :-)

Citation

Apart from citing the MolGAN paper, please also consider citing this Github page if you find this implementation assists you :-)

@misc{qin2020molgan_pytorch,
  author = {Qin, Zhenyue},
  title = {MolGAN Pytorch Implementaion},
  year = {2020},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ZhenyueQin/Implementation-MolGAN-PyTorch}}
}

Credits

This repository uses the following implementations:

• MolGAN PyTorch by YongQyu
• MolGAN TF
• Freeplk

Licence

CC-BY-4.0