Surface-based Molecular Design with Multi-modal Flow Matching
This work addresses a critical limitation in current therapeutic peptide design approaches, which often neglect the pivotal role of molecular surface properties in protein–protein interactions, thereby constraining the accuracy of binding prediction and peptide generation. To overcome this, we propose SurfFlow—the first multimodal conditional flow matching (CFM) generative model that explicitly integrates molecular surface geometry and biochemical features into de novo peptide design. SurfFlow jointly optimizes peptide sequence, structure, and surface characteristics to enable all-atom-level co-design of peptides and their receptors. Evaluated on the PepMerge benchmark, SurfFlow consistently outperforms existing all-atom generative models across all metrics, demonstrating the essential contribution of surface information to enhancing peptide binding affinity and specificity.