PPFlow: Target-aware Peptide Design with Torsional Flow Matching
<|reference_start|>PPFlow: Target-aware Peptide Design with Torsional Flow Matching: 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.<|reference_end|>
@article{lin2024ppflow:,
    title={PPFlow: Target-aware Peptide Design with Torsional Flow Matching},
    author={Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng
  Liu, Yufei Huang, Stan Z. Li},
    journal={arXiv preprint arXiv:2405.06642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06642},
    primaryClass={q-bio.BM cs.AI cs.LG}
}