Energy-Guided Test-Time Refinement for Full-Atom Peptide Binder Design

Peptide binder design generates binder sequences and structures conditioned on a target receptor. Across model architectures, scaling test-time sampling has become the dominant strategy to improve binding quality, yet it wastes computation on unpromising samples and overlooks residue-level signal. To address both, we introduce Energy-guided Test-Time Refinement (E-TTR), which reallocates the inference budget to high-potential candidates and refines them at the residue level. Concretely, given a receptor, we sample an initial pool of peptides and score each with Rosetta to obtain a binding energy together with its per-residue decomposition. For the best candidate, we re-noise only the high-energy residues and denoise them conditioned on the rest, producing a new candidate pool. This loop replaces parallel sampling with several sequential rounds, progressively improving binding quality within the same inference budget. Because standard denoisers share one timestep across the entire peptide, we post-train them via Diffusion Forcing to assign per-residue timesteps, making residue-level edits far more effective. Experiments across three benchmarks and two generative backbones demonstrate that E-TTR largely outperforms competing test-time scaling strategies in binding affinity and success rate, with up to 28% lower binding energy at matched sampling budgets.