ApexMO: Edit-Constrained Multi-Objective Optimization of Generative Peptide Proposals

Generative sequence models produce diverse peptide proposals, but refining a proposal requires balancing competing properties while controlling how far its sequence changes. We present ApexMO, a computational framework that combines length-conditioned discrete flow matching with anchor-relative, edit-constrained Pareto optimization. Generated peptides provide fixed anchors for local refinement of predicted antimicrobial activity, toxicity, and hemolysis. Under matched raw-attempt budgets, the flow-matching generator yielded approximately 1.8 times as many sequences meeting the specified novelty, predicted-activity, and ensemble-dispersion criteria as each of the two tested baseline systems. Across five refinement runs, all-survivor hypervolume of predicted activity, toxicity, and hemolysis increased by 26.8–27.9%. Under equal evaluation budgets, Pareto-based search achieved higher hypervolume than random local search, both for the final population and for all feasible solutions evaluated during a run. Adding crossover did not improve the median relative to mutation-only search. An edit-budget ablation quantified the trade-off between predicted objective improvement and anchor proximity. A screening cascade prioritized 35 DBAASP-predicted AMP candidates from 25 generated anchors, with recorded sequence ancestry. These results are based on model predictions and do not establish experimental antimicrobial activity or safety.