Artificial intelligence (AI) for reaction condition optimization has become an important topic in the pharmaceutical industry, given that a data-driven AI model can assist drug discovery and accelerate reaction design. However, existing AI models lack the chemical insights and real-time knowledge acquisition abilities of experienced human chemists. This paper proposes a Large Language Model (LLM) empowered AI agent to bridge this gap. We put forth a novel three-phase paradigm and applied advanced intelligence-enhancement methods like in-context learning and multi-LLM debate so that the AI agent can borrow human insight and update its knowledge by searching the latest chemical literature. Additionally, we introduce a novel Coarse-label Contrastive Learning (CCL) based chemical fingerprint that greatly enhances the agent's performance in optimizing the reaction condition. With the above efforts, the proposed AI agent can autonomously generate the optimal reaction condition recommendation without any human interaction. Further, the agent is highly professional in terms of chemical reactions. It demonstrates close-to-human performance and strong generalization capability in both dry-lab and wet-lab experiments. As the first attempt in the chemical AI agent, this work goes a step further in the field of "AI for chemistry" and opens up new possibilities for computer-aided synthesis planning.

翻译：人工智能（AI）在反应条件优化中已成为制药行业的重要议题，因为数据驱动的AI模型能够辅助药物发现并加速反应设计。然而，现有AI模型缺乏经验丰富的化学家所具备的化学洞察力与实时知识获取能力。本文提出一种由大语言模型（LLM）赋能的人工智能代理以弥补这一差距。我们设计了一种新颖的三阶段范式，并应用了上下文学习、多LLM辩论等高级智能增强方法，使AI代理能够借鉴人类洞察力，并通过检索最新化学文献更新其知识。此外，我们引入了一种基于粗粒度对比学习（CCL）的新型化学指纹，这极大提升了代理在优化反应条件方面的性能。基于上述努力，所提出的AI代理能够自主生成最优反应条件推荐，而无需任何人工干预。进一步地，该代理在化学反应方面表现出高度的专业性。在干实验和湿实验中都展现了接近人类的表现与强大的泛化能力。作为化学AI代理领域的首次尝试，本研究是"AI for chemistry"方向上迈出的重要一步，并为计算机辅助合成规划开辟了新可能。