Efficient 3D Molecular Generation with Flow Matching and Scale Optimal Transport

arXiv:2406.07266v1 Announce Type: new
Abstract: Generative models for 3D drug design have gained prominence recently for their potential to design ligands directly within protein pockets. Current approaches, however, often suffer from very slow sampling times or generate molecules with poor chemical validity. Addressing these limitations, we propose Semla, a scalable E(3)-equivariant message passing architecture. We further introduce a molecular generation model, MolFlow, which is trained using flow matching along with scale optimal transport, a novel extension of equivariant optimal transport. …

abstract arxiv cs.ai cs.lg cs.ne current design drug design flow generate generative generative models however ligands limitations molecules potential protein sampling scale transport type