Rethinking Molecular Design: Integrating Latent Variable and Auto-Regressive Models for Goal Directed Generation
This work addresses challenges in drug design by enhancing generative models for molecular sequences, though it appears incremental as it builds on existing methods.
The paper tackled the problem of de novo molecule design by investigating limitations of classical generative models like VAEs and auto-regressive models, proposing a hybrid model with a novel regularizer that improves validity, conditional generation, and style transfer of molecular sequences.
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.