  Therapeutic peptides have proven to have great pharmaceutical value and
potential in recent decades. However, methods of AI-assisted peptide drug
discovery are not fully explored. To fill the gap, we propose a target-aware
peptide design method called \textsc{PPFlow}, based on conditional flow
matching on torus manifolds, to model the internal geometries of torsion angles
for the peptide structure design. Besides, we establish a protein-peptide
binding dataset named PPBench2024 to fill the void of massive data for the task
of structure-based peptide drug design and to allow the training of deep
learning methods. Extensive experiments show that PPFlow reaches
state-of-the-art performance in tasks of peptide drug generation and
optimization in comparison with baseline models, and can be generalized to
other tasks including docking and side-chain packing.
