Lin Kang, Zhang Chengyun, Bai Renren, Duan Hongliang
College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, PR China.
AI department, Shanghai Highslab Therapeutics. Inc, Shanghai 201203, China.
J Med Chem. 2025 Jun 12;68(11):10577-10598. doi: 10.1021/acs.jmedchem.5c00712. Epub 2025 Jun 4.
Cyclic peptides have emerged as promising modulators of protein-protein interactions due to their unique pharmacological properties and ability to target extensive flat binding interfaces. However, traditional strategies for developing cyclic peptides are often hindered by significant resource constraints. Recent advancements in computational techniques and artificial intelligence-driven methodologies have significantly enhanced the cyclic peptide drug discovery pipeline, while breakthroughs in automated synthesis platforms have accelerated experimental validation, presenting transformative potential for pharmaceutical innovation. In this review, we examine state-of-the-art computational and artificial intelligence-driven strategies that address challenges such as peptide flexibility, limited data availability, and complex conformational landscapes. We discuss how the integration of physics-based simulations with deep learning techniques is redefining the design and optimization of cyclic peptide therapeutics and propose future perspectives to advance the precision and efficiency of cyclic peptide drug development, ultimately offering innovative solutions to unmet medical needs.
由于其独特的药理特性以及靶向广泛平面结合界面的能力，环肽已成为蛋白质-蛋白质相互作用的有前景的调节剂。然而，开发环肽的传统策略常常受到大量资源限制的阻碍。计算技术和人工智能驱动方法的最新进展显著增强了环肽药物发现流程，而自动化合成平台的突破加速了实验验证，为药物创新展现出变革潜力。在本综述中，我们研究了最先进的计算和人工智能驱动策略，这些策略应对了诸如肽的灵活性、有限的数据可用性和复杂的构象格局等挑战。我们讨论了基于物理的模拟与深度学习技术的整合如何重新定义环肽疗法的设计和优化，并提出了推进环肽药物开发的精度和效率的未来展望，最终为未满足的医疗需求提供创新解决方案。
