StapleBridge: Finite-Support Intervention Control for Stapled Peptide Optimization

Hydrocarbon stapling covalently links two peptide side chains to constrain peptide conformation and can improve proteolytic stability and cellular uptake. Recent computational cyclic-peptide design has largely focused on de novo generation, yet redesigning a functional peptide from scratch can perturb sequence and structural context important for its function. Constrained local editing requires a framework to select chemically and geometrically valid modifications despite scarce paired linear-to-stapled supervision. To address this, we introduce **StapleBridge**, a chemistry-aware discrete Schr\"odinger-control framework for optimizing existing peptides through hydrocarbon stapling. For each peptide, StapleBridge constructs a finite set of executable interventions and maps each to a canonical minimal-edit product, making the optimal intervention distribution exactly computable from terminal utility. Distilling this distribution into an amortized controller provides plan-level supervision without paired examples and enables efficient intervention selection on unseen peptides. On test leads supported by the fixed, property-independent intervention catalog, StapleBridge improves predicted permeability for 81.13% of leads, with a mean permeability gain of 0.169 compared with 0.140 for the strongest matched plan-level baseline. Cross-predictor permeability evaluation and an orthogonal, post-selection 3D evaluation support our observed predicted gains and structural compatibility, while computational experiments on N-methylation and disulfide cyclization support extension to additional chemistries. Overall, StapleBridge enables property-guided refinement of existing peptide leads by learning which feasible chemical edits to apply without paired linear-to-stapled supervision. Code is available at https://anonymous.4open.science/r/Staplebridge-A05A/.