Can Agent Understand the Differences Between Functionally Distinct Peptides? A Self-Evolving Bifunctional Peptide Discovery Engine

Designing peptides that both bind a protein target and kill bacteria means navigating trade-offs no single objective captures: the cationicity that improves antimicrobial potency also drives hemolysis, and edits that raise membrane activity tend to erode binding. We present a self-evolving agent that pairs LLM-driven sequence redesign with physical evaluators it cannot argue past — interface energetics, MIC prediction, and hemolysis prediction. Starting from de novo binder backbones, the agent diagnoses deficits along three axes, proposes edits with explicit predicted outcomes, and accumulates transferable rules in persistent memory. Because it commits to a prediction before each evaluation, its stated reasoning can be audited rather than assumed.