MADD：多智能体药物发现协同系统

摘要

命中化合物识别是早期药物研发的核心挑战，传统方法需要耗费大量实验资源。人工智能尤其是大语言模型（LLMs）的最新进展，使得能够通过虚拟筛选方法降低成本并提升效率。然而，这些工具日益增长的复杂性限制了湿实验室研究人员的使用门槛。多智能体系统通过将LLMs的可解释性与专业模型工具的精确性相结合，提供了颇具前景的解决方案。本研究提出MADD多智能体系统，能够根据自然语言查询构建并执行定制化的命中化合物识别流程。该系统采用四个协同智能体处理从头化合物生成与筛选中的关键子任务。我们在七个药物研发案例中评估MADD，证明其性能优于现有基于LLM的解决方案。借助MADD，我们率先将AI优先的药物设计方法应用于五个生物靶点，并公布了已识别的命中分子。最后，我们建立了包含300多万个化合物的查询-分子对及其对接评分的新基准数据集，以推动药物设计向智能体化方向发展。

English

Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particularly large language models (LLMs), have enabled virtual screening methods that reduce costs and improve efficiency. However, the growing complexity of these tools has limited their accessibility to wet-lab researchers. Multi-agent systems offer a promising solution by combining the interpretability of LLMs with the precision of specialized models and tools. In this work, we present MADD, a multi-agent system that builds and executes customized hit identification pipelines from natural language queries. MADD employs four coordinated agents to handle key subtasks in de novo compound generation and screening. We evaluate MADD across seven drug discovery cases and demonstrate its superior performance compared to existing LLM-based solutions. Using MADD, we pioneer the application of AI-first drug design to five biological targets and release the identified hit molecules. Finally, we introduce a new benchmark of query-molecule pairs and docking scores for over three million compounds to contribute to the agentic future of drug design.

MADD：多智能体药物发现协同系统

MADD: Multi-Agent Drug Discovery Orchestra

摘要

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