Agent-Guided Ranking Policy Improvement for Peptide Drug Candidate Prioritization
Wijaya, E.
Show abstract
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 scored on all four endpoints, the agent-derived policy captures 65% of the best candidates in its top-20 shortlist, compared to 44% for NSGA-II and 61% for both equal-weight scalarization and best-of-1,000 random weight search (Wilcoxon signed-rank p = 0.004 across 10 independent data splits). We report on a public antimicrobial benchmark, not a clinical claim. The code, oracles, and ranking policy are released as a drop-in triage layer: the candidate pool can be swapped with an internal peptide library and in-house assays.
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