Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation

TL;DR

This paper revisits fundamental molecular generative models, proposing a hybrid approach that combines VAEs and auto-regressive models to enhance validity, conditional generation, and style transfer in drug design.

Contribution

It introduces a novel regularizer that integrates the strengths of VAEs and auto-regressive models for improved molecular generation.

Findings

Enhanced validity of generated molecules

Improved conditional generation capabilities

Better style transfer in molecular sequences

Abstract

De novo molecule design has become a highly active research area, advanced significantly through the use of state-of-the-art generative models. Despite these advances, several fundamental questions remain unanswered as the field increasingly focuses on more complex generative models and sophisticated molecular representations as an answer to the challenges of drug design. In this paper, we return to the simplest representation of molecules, and investigate overlooked limitations of classical generative approaches, particularly Variational Autoencoders (VAEs) and auto-regressive models. We propose a hybrid model in the form of a novel regularizer that leverages the strengths of both to improve validity, conditional generation, and style transfer of molecular sequences. Additionally, we provide an in depth discussion of overlooked assumptions of these models' behaviour.