  Deep generative models that produce novel molecular structures have the
potential to facilitate chemical discovery. Diffusion models currently achieve
state of the art performance for 3D molecule generation. In this work, we
explore the use of flow matching, a recently proposed generative modeling
framework that generalizes diffusion models, for the task of de novo molecule
generation. Flow matching provides flexibility in model design; however, the
framework is predicated on the assumption of continuously-valued data. 3D de
novo molecule generation requires jointly sampling continuous and categorical
variables such as atom position and atom type. We extend the flow matching
framework to categorical data by constructing flows that are constrained to
exist on a continuous representation of categorical data known as the
probability simplex. We call this extension SimplexFlow. We explore the use of
SimplexFlow for de novo molecule generation. However, we find that, in
practice, a simpler approach that makes no accommodations for the categorical
nature of the data yields equivalent or superior performance. As a result of
these experiments, we present FlowMol, a flow matching model for 3D de novo
generative model that achieves improved performance over prior flow matching
methods, and we raise important questions about the design of prior
distributions for achieving strong performance in flow matching models. Code
and trained models for reproducing this work are available at
https://github.com/dunni3/FlowMol
