PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Lin, H.; Zhang, O.; Zhao, H.; Jiang, D.; Wu, L.; Liu, Z.; Huang, Y.; Li, S. Z.
Show abstract
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 PPFO_SCPLOWLOWC_SCPLOW, 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 PPFO_SCPLOWLOWC_SCPLOW 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.
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