DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery

Molecular screening and optimization require agents to coordinate specialized computation and adapt their actions as evidence develops. We introduce DrugHarness, a unified system for composable scientific execution and experience-driven strategy learning. DrugHarness integrates over 80 scientific operations with biomedical resources and executable scientific code, and translates execution experience into skills with explicit applicability conditions. An independent review agent examines candidate skills against source evidence and targeted scientific trials when needed; admitted skills guide subsequent tasks and are revised using their feedback. Without evolution, DrugHarness achieves 91.9% affinity-comparison accuracy and 100% molecular-editing accuracy on MolBench. With frozen learned guidance, the Qwen configuration reaches 81.1% affinity-comparison accuracy on development tasks, compared with a historical no-skill result of 62.2%. In an HPK1 design task, a DrugHarness-designed compound was synthesized and experimentally evaluated by medicinal chemistry collaborators, achieving a cellular IC50 of 14.19\ nM versus 37.47\ nM for the best input reference. These results connect broad scientific execution, experience-derived guidance, and candidate-level computational and experimental evaluation in a unified approach to drug discovery.