AI智能体可自主完成药物发现全流程，显著提速降本。
AI Agents in Drug Discovery
- 基于大模型与感知、行动、记忆能力构建自主科研智能体。
- 实测将原需数月的药物研发流程压缩至数小时，提升效率与可复现性。
- 适合药企研发团队、科研机构探索自动化药物发现新范式。
人工智能（AI）智能体正成为药物发现领域的变革性工具，具备在复杂研究流程中自主推理、执行与学习的能力。依托大型语言模型（LLMs）结合感知、计算、行动和记忆功能，这类智能体系统可整合多源生物医学数据，通过机器人平台执行实验任务，并在闭环中迭代优化假设。本文系统梳理了从ReAct、Reflection到Supervisor与Swarm等智能体架构，展示其在文献综述、毒性预测、自动化实验方案生成、小分子合成、药物重定位及端到端决策等关键阶段的应用。据我们所知，这是首篇系统呈现实际部署并量化评估智能体在药物发现工作中真实效能的综述。早期应用显示，该技术在速度、可复现性和可扩展性方面均有显著提升，使原本耗时数月的流程缩短至数小时，同时保障科学可追溯性。文章还讨论了数据异构性、系统可靠性、隐私保护与基准测试等挑战，并展望未来技术如何更好地支撑科学研究与转化。
原文摘要 · Abstract (English)
Artificial intelligence (AI) agents are emerging as transformative tools in drug discovery, with the ability to autonomously reason, act, and learn through complicated research workflows. Building on large language models (LLMs) coupled with perception, computation, action, and memory tools, these agentic AI systems could integrate diverse biomedical data, execute tasks, carry out experiments via robotic platforms, and iteratively refine hypotheses in closed loops. We provide a conceptual and technical overview of agentic AI architectures, ranging from ReAct and Reflection to Supervisor and Swarm systems, and illustrate their applications across key stages of drug discovery, including literature synthesis, toxicity prediction, automated protocol generation, small-molecule synthesis, drug repurposing, and end-to-end decision-making. To our knowledge, this represents the first comprehensive work to present real-world implementations and quantifiable impacts of agentic AI systems deployed in operational drug discovery settings. Early implementations demonstrate substantial gains in speed, reproducibility, and scalability, compressing workflows that once took months into hours while maintaining scientific traceability. We discuss the current challenges related to data heterogeneity, system reliability, privacy, and benchmarking, and outline future directions towards technology in support of science and translation.
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