FastJTNNpy3 : Junction Tree Variational Autoencoder for Molecular Graph Generation

Python 3 Version of Fast Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

<img src="https://github.com/Bibyutatsu/FastJTNNpy3/blob/master/Old/paradigm.png" width="600">

Implementation of our Junction Tree Variational Autoencoder https://arxiv.org/abs/1802.04364

Requirements

• RDKit (version >= 2017.09) : Tested on 2019.09.1
• Python (version >= 3.6) : Tested on 3.7.4
• PyTorch (version >= 0.2) : Tested on 1.0.1

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.

Quick Start

Code for Accelerated Training

This repository contains the Python 3 implementation of the new Fast Junction Tree Variational Autoencoder code.

• fast_molvae/ contains codes for VAE training. Please refer to fast_molvae/README.md for details.
• fast_jtnn/ contains codes for model implementation.
• fast_bo/ contains codes for Bayesian Optimisation (WIP: support for custom rdkit functions).
• fast_molopt/ contains codes for molecule optimisation using a JTpropVAE which is the same as JTVAE but also enmeds properties with the molecules. (WIP: integration in main pipeline)

Old codes

This repository contains the following directories:

• Old/bo includes scripts for Bayesian optimization experiments. Please read Old/bo/README.md for details.
• Old/molvae/ includes scripts for training our VAE model only. Please read Old/molvae/README.md for training our VAE model.
• Old/molopt/ includes scripts for jointly training our VAE and property predictors. Please read Old/molopt/README.md for details.
• Old/molvae/jtnn/ contains codes for model formulation. Please read Old/molvae/README.md for training our VAE model.

Contact

Bibhash Chandra Mitra (bibhashm220896@gmail.com)