PPFlow: Target-aware Peptide Design with Torsional Flow Matching

• URL: http://arxiv.org/abs/2405.06642v3
• Date: Sun, 16 Jun 2024 11:33:54 GMT
• Title: PPFlow: Target-aware Peptide Design with Torsional Flow Matching
• Authors: Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li,
• Abstract summary: We propose a target-aware peptide design method called textscPPFlow 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.
• Score: 52.567714059931646
• License: http://creativecommons.org/licenses/by/4.0/
• 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 \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.

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