تقرير
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
| العنوان: | PPFlow: Target-aware Peptide Design with Torsional Flow Matching |
|---|---|
| المؤلفون: | Lin, Haitao, Zhang, Odin, Zhao, Huifeng, Jiang, Dejun, Wu, Lirong, Liu, Zicheng, Huang, Yufei, Li, Stan Z. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 18 pages |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.06642 |
| رقم الأكسشن: | edsarx.2405.06642 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |