Agent-Guided Ranking Policy Improvement for Peptide Drug Candidate Prioritization (opens in new tab)

Peptide drug programs live or die on triage: picking the handful of candidates worth expensive wet-lab validation from thousands of in silico hits, under competing activity, toxicity, stability, and developability constraints. We asked whether an automated policy-search agent, given a frozen evaluation harness and a scored candidate pool, could learn a better ranking policy than the weighted-sum score a human team would write by hand. Across a public benchmark of 3,554 antimicrobial peptides ...