Junction Tree Variational Autoencoder for Molecular Graph Generation
Official implementation of our Junction Tree Variational Autoencoder https://arxiv.org/abs/1802.04364
We have accelerated our code! The new code is in
fast_jtnn/, and the VAE training script is in
fast_molvae/. Please refer to
fast_molvae/README.md for details.
- Linux (We only tested on Ubuntu)
- RDKit (version >= 2017.09)
- Python (version == 2.7)
- PyTorch (version >= 0.2)
To install RDKit, please follow the instructions here http://www.rdkit.org/docs/Install.html
We highly recommend you to use conda for package management.
The following directories contains the most up-to-date implementations of our model:
fast_jtnn/contains codes for model implementation.
fast_molvae/contains codes for VAE training. Please refer to
The following directories provides scripts for the experiments in our original ICML paper:
bo/includes scripts for Bayesian optimization experiments. Please read
molvae/includes scripts for training our VAE model only. Please read
molvae/README.mdfor training our VAE model.
molopt/includes scripts for jointly training our VAE and property predictors. Please read
jtnn/contains codes for model formulation.
Wengong Jin (firstname.lastname@example.org)