该数据为论文「De novo design of peptide binders to conformationally diverse targets with contrastive language modeling」（已于 2025 年 1 月发布于 Science Advances）所有原始和处理后的数据。该论文杜克大学的研究团队发布的成果，构建了基于 CLIP 的肽优先级筛选流程 PepPrCLIP，可以设计短蛋白质以结合和破坏以前无法用药的致病蛋白质。与使用目标 3D 结构生成肽的现有平台 RFDiffusion 相比，PepPrCLIP 速度更快，并且能够创建几乎总是与目标蛋白质更匹配的肽。

Citation

@misc{bhat_2024_13917484, author = {Bhat, Suhaas and Palepu, Kalyan and Hong, Lauren and Mao, Joey and Ye, Tianzheng and Iyer, Rema and Zhao, Lin and Chen, Tianlai and Vincoff, Sophia and Watson, Rio and Wang, Tian Z. and Srijay, Divya and Venkata, Srikar Kavirayuni and Kholina, Kseniia and Goel, Shrey and Vure, Pranay and Desphande, Aniruddha J. and Soderling, Scott H. and DeLisa, Matthew P. and Chatterjee, Pranam}, title = {De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling }, year = 2024, publisher = {Zenodo}, doi = {10.5281/zenodo.13917484}, url = {https://doi.org/10.5281/zenodo.13917484}, }