Learning from Experience for Multi-Objective Antimicrobial Peptide Design

Antimicrobial peptides (AMPs) are promising candidates for drug-resistant infections, but practical design must balance antimicrobial activity, pathogen-specific potency, hemolytic safety, and toxicity. Conflicts among these objectives make it difficult to identify peptides with balanced property profiles. Navigating such trade-offs motivates the reuse of prior design experience under different objective bottlenecks. However, existing methods do not explicitly leverage successful and failed design cases as reusable experience during iterative optimization. To address this gap, we introduce PepMind, an experience-guided framework for iterative multi-objective AMP optimization. PepMind converts evaluated designs into structured records containing sequences, objective outcomes, design rationales, and failure diagnoses. A learned router is used to select the type and amount of experience according to the current optimization state for targeted sequence refinement, with new outcomes subsequently added to memory. Experimental results consistently demonstrate PepMind's effectiveness: it achieves the best performance in 19 of 20 evaluation settings and improves four-objective hypervolume (HV) by an average of 67.1% over the strongest external baseline. Further analyses confirm cross-pathogen experience transfer and favorable predicted properties of the generated peptides. These results support persistent, state-conditioned experience reuse as an effective strategy for adaptive multi-objective peptide design.