DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery

Key points

• Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints.1
• From execution trajectories and outcome feedback, DrugHarness induces action hypotheses with observable applicability conditions, recommended actions and supporting evidence.1
• Evaluation on MolBench and SMDD-Bench demonstrates capabilities in molecular screening, optimization and constrained design.1
• These results support conditional skill evolution for scientific agents and demonstrate a workflow connecting computational drug design with experimental validation.1

Sources (1)

• [1]DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 05:20 PM

  Here we introduce DrugHarness, an evolvable agent harness coupling composable scientific execution with conditional skill induction.

  Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints.

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