PPDiff: Position-Priority-Aware Masked Diffusion for Controllable Peptide Generation

Controllable peptide generation requires careful refinement of residues that determine the requested properties. Masked discrete diffusion offers flexible generation trajectories, but uniform training corruption allocates reconstruction practice independently of downstream property relevance. We introduce PPDiff, a controllable generation framework that uses an external property planner at both stages. A lightweight sequence random forest with in silico mutagenesis estimates residue criticality and uncertainty. The resulting prior reallocates a fixed training mask budget and, during sampling, is combined with denoiser confidence, margin, and temporal stability to decide when each residue is ready to commit. The selected pretrained masked-diffusion backbone is retained without architectural changes, and models used for evaluation are separate from the guide. Across permeability, hemolysis safety, and nonfouling, PPDiff improves the joint objective under three separate sequence evaluators while supporting target-specific candidate design in computational screens against seven proteins. A multi-seed factorial study and a shuffled-prior control further show that the main gain arises when property-bearing position information coordinates both training corruption and sampling commitment.