MADD：多智能體藥物發現協奏曲

摘要

命中化合物識別是早期藥物研發的核心挑戰，傳統上需要耗費大量實驗資源。人工智慧（尤其是大型語言模型）的最新進展，使得虛擬篩選方法能夠有效降低成本並提升效率。然而，這類工具日益增長的複雜性限制了濕實驗室研究人員的使用門檻。多智能體系統通過結合大型語言模型的可解釋性與專業模型及工具的精准度，為此提供了極具前景的解決方案。本研究提出MADD多智能體系統，能根據自然語言查詢構建並執行客製化的命中化合物識別流程。MADD採用四個協同運作的智能體，分別處理從頭化合物生成與篩選中的關鍵子任務。我們在七個藥物研發案例中評估MADD，證明其性能優於現有基於大型語言模型的解決方案。通過MADD系統，我們率先將AI優先的藥物設計方法應用於五個生物靶點，並公開發布所識別的命中分子。最後，我們建立了包含逾三百萬個化合物的查詢-分子對接評分新基準，以推動藥物設計邁向智能體驅動的未來。

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：多智能體藥物發現協奏曲

MADD: Multi-Agent Drug Discovery Orchestra

摘要

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