  Peptides, short chains of amino acid residues, play a vital role in numerous
biological processes by interacting with other target molecules, offering
substantial potential in drug discovery. In this work, we present PepFlow, the
first multi-modal deep generative model grounded in the flow-matching framework
for the design of full-atom peptides that target specific protein receptors.
Drawing inspiration from the crucial roles of residue backbone orientations and
side-chain dynamics in protein-peptide interactions, we characterize the
peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$
manifold and side-chain angles on high-dimensional tori. Furthermore, we
represent discrete residue types in the peptide sequence as categorical
distributions on the probability simplex. By learning the joint distributions
of each modality using derived flows and vector fields on corresponding
manifolds, our method excels in the fine-grained design of full-atom peptides.
Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks
such as fix-backbone sequence design and side-chain packing through partial
sampling. Through meticulously crafted experiments, we demonstrate that PepFlow
exhibits superior performance in comprehensive benchmarks, highlighting its
significant potential in computational peptide design and analysis.
