Molecular Generative Adversarial Network with Multi-Property Optimization

TL;DR

This paper introduces InstGAN, a novel actor-critic RL-based GAN for molecule generation that optimizes multiple properties efficiently, overcoming training instability and scalability issues of previous models.

Contribution

The study proposes InstGAN, a new GAN framework utilizing actor-critic RL with global rewards and entropy maximization for improved molecular generation.

Findings

Outperforms baseline models in molecule generation tasks

Achieves comparable results to state-of-the-art methods

Efficiently generates molecules with multiple property optimizations

Abstract

Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery. Most prior studies have utilized reinforcement learning (RL) algorithms, particularly Monte Carlo tree search (MCTS), to handle the discrete nature of molecular representations in GANs. However, due to the inherent instability in training GANs and RL models, along with the high computational cost associated with MCTS sampling, MCTS RL-based GANs struggle to scale to large chemical databases. To tackle these challenges, this study introduces a novel GAN based on actor-critic RL with instant and global rewards, called InstGAN, to generate molecules at the token-level with multi-property optimization. Furthermore, maximized information entropy is leveraged to alleviate the mode collapse. The experimental results demonstrate that InstGAN outperforms other baselines, achieves comparable performance to state-of-the-art models, and efficiently generates molecules with multi-property optimization. The source code will be released upon acceptance of the paper.