Navigating Chemical Space with Latent Flows

TL;DR

ChemFlow introduces a novel framework for navigating chemical space by learning vector fields in latent space, enabling efficient molecule manipulation and optimization for drug discovery and materials science.

Contribution

It unifies previous latent space traversal methods and proposes new approaches with physical priors, advancing molecular exploration techniques.

Findings

Effective molecule manipulation demonstrated

Multi-objective optimization achieved

Framework validated on supervised and unsupervised tasks

Abstract

Recent progress of deep generative models in the vision and language domain has stimulated significant interest in more structured data generation such as molecules. However, beyond generating new random molecules, efficient exploration and a comprehensive understanding of the vast chemical space are of great importance to molecular science and applications in drug design and materials discovery. In this paper, we propose a new framework, ChemFlow, to traverse chemical space through navigating the latent space learned by molecule generative models through flows. We introduce a dynamical system perspective that formulates the problem as learning a vector field that transports the mass of the molecular distribution to the region with desired molecular properties or structure diversity. Under this framework, we unify previous approaches on molecule latent space traversal and optimization and propose alternative competing methods incorporating different physical priors. We validate the efficacy of ChemFlow on molecule manipulation and single- and multi-objective molecule optimization tasks under both supervised and unsupervised molecular discovery settings. Codes and demos are publicly available on GitHub at https://github.com/garywei944/ChemFlow.