FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation.

ABSTRACT: A generative model capable of sampling realistic molecules with desired properties could accelerate chemical discovery across a wide range of applications. Toward this goal, significant effort has focused on developing models that jointly sample molecular topology and 3D structure. We present FlowMol3, an open-source, multi-modal flow matching model that advances the state of the art for all-atom, small-molecule generation. Its substantial performance gains over previous FlowMol versions are achieved without changes to the graph neural network architecture or the underlying flow matching formulation. Instead, FlowMol3's improvements arise from three architecture-agnostic techniques that incur negligible computational cost: self-conditioning, fake atoms, and train-time geometry distortion.

SUBMITTER: Dunn I 

PROVIDER: S-EPMC12393239 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature