HFGuidedDesign: de novo design of cyclic peptide binders via structure-guided discrete diffusion

Cyclic peptides are promising scaffolds for targeting protein surfaces due to their unique structural and functional advantages. However, the limited availability of cyclic peptide–protein complex structures severely restricts the design of target-specific cyclic peptides. Here, we introduce HFGuidedDesign, a de novo cyclic peptide design framework that integrates a discrete diffusion model with external structure guidance. By incorporating the high-accuracy complex structure predictor HighFold, the framework performs real-time structural evaluation during reverse diffusion sampling and dynamically steers sequence generation toward cyclic peptides with favorable structural plausibility and binding potential. The discrete diffusion model is trained using a two-stage strategy, including pre-training on peptide monomers and fine-tuning on peptide–protein complex structures. In design tasks targeting two distinct proteins, we evaluate two classical cyclization strategies—head-to-tail and disulfide bond cyclization. The resulting cyclic peptides achieved sequence design success rates of 75% and 66.7% for the two targets, demonstrating the effectiveness and generalizability of the framework. This study establishes an innovative and scalable computational framework for sequence-based cyclic peptide design, facilitating the development of peptide-based ligands for protein targeting.