Yunxiang Yu, Zhou Zhang, Hai Guo, Xinlu Ren, Yuting Zhang, Jianna Meng, Yi Zhou, Jian Han, Jinhui Tian, Wenjin Yan, Jinqi Huang
School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, Gansu, China.
The Second Clinical Medical School, Lanzhou University, Lanzhou, 730000, Gansu, China.
J Comput Aided Mol Des. 2025 Aug 9;39(1):63. doi: 10.1007/s10822-025-00639-8.
Cyclic peptides, prized for their remarkable bioactivity and stability, hold great promise across various fields. Yet, designing membrane-penetrating bioactive cyclic peptides via traditional methods is complex and resource-intensive. To address this, we introduce CCPep, an AI-driven de novo design framework that combines reinforcement and contrastive learning for efficient, customizable membrane-penetrating cyclic peptide design. It assesses peptide membrane penetration with scoring models and optimizes transmembrane ability through reinforcement learning. Customization of peptides with specific properties is achieved via custom functions, while contrastive learning incorporates molecular dynamics simulation time series to capture dynamic penetration features, enhancing model performance. Result shows that CCPep generated cyclic peptide sequences have a promising membrane penetration rate, with customizable chain length, natural amino acid ratio, and target segments. This framework offers an efficient tool for cyclic peptide drug design and paves the way for AI-driven multi-objective molecule design.
环肽因其卓越的生物活性和稳定性而备受珍视，在各个领域都具有巨大的潜力。然而，通过传统方法设计具有膜穿透性的生物活性环肽既复杂又耗费资源。为了解决这一问题，我们引入了CCPep，这是一个由人工智能驱动的从头设计框架，它结合了强化学习和对比学习，用于高效、可定制的膜穿透环肽设计。它使用评分模型评估肽的膜穿透能力，并通过强化学习优化跨膜能力。通过自定义函数实现具有特定属性的肽的定制，而对比学习则结合分子动力学模拟时间序列来捕捉动态穿透特征，从而提高模型性能。结果表明，CCPep生成的环肽序列具有良好的膜穿透率，具有可定制的链长、天然氨基酸比例和目标片段。该框架为环肽药物设计提供了一个高效的工具，并为人工智能驱动的多目标分子设计铺平了道路。
