DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery· 药物发现的计算框架
Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHarness, an evolvable agent harness coupling composable scientific execution with conditional skill induction. Its environment integrates over 80 scientific operations, biomedical resources and task-specific code, preserving associations among candidates, results and artifacts. From execution trajectories and outcome feedback, DrugHarness induces action hypotheses with observable applicability conditions, recommended actions and supporting evidence. An independent review agent evaluates candidate skills derived from these hypotheses through source verification and, when needed, targeted execution trials. Admitted skills guide subsequent tasks a
计算药物发现框架，可进化以适应变化的证据和约束。
- 核心方法
- 提出DrugHarness框架，结合可组合的科学执行与条件技能诱导，通过执行轨迹和结果反馈生成可行的行动假设，并由独立的审查代理评估和验证这些假设。
- 适合谁读
- 适合计算生物学、药物发现领域的研究者和工程师阅读。
- 要解决的问题
- 计算药物发现过程中需要灵活组合科学操作并适应新的证据和约束条件。
- 关键实验
- 未提供
- 主要贡献
- 提供了80多个科学操作、生物医学资源和特定任务代码的集成环境，支持药物发现过程中的技能进化和任务指导。
- 意义与局限
- 意义在于促进计算药物发现的自动化和智能化，影响药物研发的效率；局限在于实际应用效果需进一步验证。