MolGAN: An implicit generative model for small molecular graphs

TL;DR

MolGAN is a novel likelihood-free generative adversarial network that directly creates small molecular graphs, achieving high validity and enabling property-specific molecule generation without complex matching procedures.

Contribution

It introduces MolGAN, the first implicit, likelihood-free GAN for molecular graphs, combining graph-based generation with reinforcement learning for property optimization.

Findings

Achieves nearly 100% valid molecule generation on QM9.

Outperforms string-based methods in validity and quality.

Susceptible to mode collapse, highlighting areas for improvement.

Abstract

Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse. Code at https://github.com/nicola-decao/MolGAN