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.

基于条件流匹配对环面进行建模的针对靶向感知的肽设计方法 PPFlow 填补了人工智能辅助肽药物发现领域的不足，并通过建立名为 PPBench2024 的蛋白 - 肽结合数据集以填充大规模数据空白，实验证明 PPFlow 在肽药物的生成和优化等任务上具有领先水平的性能，并且可以推广到其他任务，包括对接和侧链装配。