CPSea2: Composing Terminal Geometries for Structurally Diverse Cyclic Peptide Binder Design

Cyclic peptides are attractive next-generation therapeutics. In drug discovery, diverse ring-closing chemistries are applied to tune synthetic accessibility and conformational constraints. Current machine learning methods represent cyclization through modified positional encodings, covalent-bond prompts in all-atom models, residue-level distance guidance, or direct terminal closure by molecular mechanics (MM). These approximations provide limited control over the raw geometry, especially for non-natural linkers, and often yield unreliable structures. Here, we introduce **CPSea2**, a composable framework that abstracts cyclization as terminal-amide geometry constraints. We construct **CPSea2-Cap** (**CPcap**), a conformer library covering eight cyclization types, and **CPSea2-Base** (**CPbase**), a large-scale dataset of nearly 900 million mined linear peptide–protein complexes. Matching terminal-amides between CPcap and CPbase directly derives cyclic peptide–protein complexes with diverse terminal topologies. Built on CPbase, **PepAny** generates target-conditioned peptide binders with controllable terminal-amide geometry, expanding the accessible space beyond mined structures. Multi-target evaluations show improved raw-output plausibility over bond-prompted and distance-guided baselines. CPSea2 provides a novel framework and data foundation for flexible and diverse cyclic peptide binder design. Datasets and codes are available at https://anonymous.4open.science/r/CPSea2-6CF5/.