  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.
