PepMap: MultiModal Flow Maps for Few-Step All-Atom Peptide Binder Design

Designing peptide binders for a target protein requires generating amino-acid sequences and bound conformations that are compatible with its binding site. Recent diffusion and flow-matching methods can jointly generate peptide sequences and structures, but their iterative sampling makes candidate generation costly. Few-step sampling is challenging because residue identities and backbone geometry must remain coordinated across large time intervals. We introduce $\text{PepMap}$, a pocket-conditioned multimodal Flow Map for few-step peptide sequence–structure co-design. PepMap learns finite-time transitions that jointly update continuous sequence states, residue positions, and backbone orientations. Flow-matching supervision guides local predictions, while multimodal semigroup consistency encourages agreement between direct and composed transitions. Geometric objectives regularize backbone geometry and peptide–pocket compatibility, and a conditional side-chain packing module produces full-atom peptide structures. On the LNR benchmark, PepMap improves structural recovery over iterative baselines at their standard sampling budgets while requiring substantially fewer network evaluations. These results support finite-time transport of coupled sequence and backbone states for efficient pocket-conditioned peptide design. Code is available at https://anonymous.4open.science/r/PepMap-anonymous-E018/.