ReachDiff: Future-Feasibility-Guided Search for Constrained Cyclic Peptide Design

Designing cyclic peptides requires several coupled requirements to be satisfied simultaneously, including chemical validity, cyclization-topology realization, and closure geometry. Their intersection defines a substantially narrower design space than any individual requirement, making efficient exploration particularly important. Most generative pipelines nevertheless spend the majority of sampling computation before determining whether a completed molecule satisfies the joint feasibility criteria. We introduce **ReachDiff**, a masked-diffusion approach that predicts future feasibility, defined as the probability that a partial molecular state will satisfy the benchmark joint-feasibility objective when generation continues with the frozen base model. A lightweight predictor scores competing partial states once during generation. The highest-scoring states receive the remaining continuation budget, after which standard denoising resumes without further guidance. Across head-to-tail, disulfide, and isopeptide-lactam cyclization, ReachDiff improves joint feasibility from 59.61% to 77.59% under an equal terminal-evaluation budget, and the advantage is retained under matched recorded model work. Future feasibility is predictable early in denoising, and controlled analyses attribute the gain to state-dependent ranking and global competition. ReachDiff also collects verifier-positive trajectories more efficiently, which can be reused for denoising alignment to improve search-free generation. Together, these results support future-feasibility-guided allocation as an effective use of finite inference compute.