HyBridge: Receptor-Guided Cyclic-Peptide Generation on Compiled Covalent Support

Cyclic peptides offer attractive scaffolds for protein-targeted therapeutics by coupling rich amino-acid chemistry with a constrained conformational landscape. However, existing generative methods using geometric conditioning or cyclic encodings leave ring constraints unenforced during the sampling process. These geometric inconsistencies can compromise both the physical validity and receptor compatibility of their generated designs. Here, we propose HyBridge, a hybrid sampler that preserves compiled covalent constraints throughout joint sequence–structure generation. HyBridge leverages a receptor-free reference model to capture intrinsic peptide diversity. Following geometric placement, a shared receptor potential guides backbone motion and residue substitutions, with accepted updates preserving the compiled constraints. To learn pairing preferences, we incorporate crossed peptide–receptor panels alongside chemical supervision, canceling additive single-chain scoring offsets. Evaluated on held-out receptor targets under a head-to-tail (H2T) benchmark, HyBridge achieves 61.2% structural delivery under a common closure, contact, and severe-overlap criterion. Specifically, suffix guidance improves delivery by 15.1 percentage points over execution without the learned potential and by 5.6 percentage points over terminal-only use. Experiments demonstrate receptor-responsive cyclic-peptide generation with persistent ring constraints and improved structural delivery, complemented by construction examples spanning disulfide, lactam, and bicyclic topologies.