Multi-Agent Drug Discovery from DNA-Encoded Library Screening: An Executable AI4Science Skill
We present a fully executable, multi-agent computational pipeline for small-molecule hit identification and compound triage from molecular screening data. Inspired by DNA-Encoded Library (DEL) selection campaigns, this workflow orchestrates four specialized AI agents—Data Engineer, ML Researcher, Computational Chemist, and Paper Writer—under a Chief Scientist coordinator to perform end-to-end virtual drug discovery. Using the MoleculeNet HIV dataset (41,127 compounds, ~3.5% active), our pipeline achieves an AUC-ROC of 0.8095 and an 8.82× enrichment factor in the top-500 predicted actives. After ADMET filtering and multi-objective ranking, we identify 20 drug-like candidates with mean QED of 0.768, mean synthetic accessibility score of 2.83, and 100% Lipinski compliance. Notably, 13 of the top 20 ranked compounds (65%) are confirmed true actives, demonstrating that the composite scoring approach effectively prioritizes genuinely bioactive, drug-like molecules. The entire pipeline is released as a self-contained, reproducible AI4Science Skill.