1.D.169.  The Membrane-penitrating Cyclic Peptide (MpCP) Family  

Cyclic peptides are prized for their remarkable bioactivity and stability. CCPep, is an AI-driven de novo design framework that combines reinforcement and contrastive learning for efficient, customizable membrane-penetrating cyclic peptide design (Yunxiang et al. 2025). 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. Results show 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 (Yunxiang et al. 2025).