Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis
Recent AI research plots a promising future of automatic chemical reactions within the chemistry society. This study proposes Chemist-X, a transformative AI agent that automates the reaction condition recommendation (RCR) task in chemical synthesis with retrieval-augmented generation (RAG) technology. To emulate expert chemists' strategies when solving RCR tasks, Chemist-X utilizes advanced RAG schemes to interrogate online molecular databases and distill critical data from the latest literature database. Further, the agent leverages state-of-the-art computer-aided design (CAD) tools with a large language model (LLM) supervised programming interface. With the ability to utilize updated chemical knowledge and CAD tools, our agent significantly outperforms conventional synthesis AIs confined to the fixed knowledge within its training data. Chemist-X considerably reduces chemists' workload and allows them to focus on more fundamental and creative problems, thereby bringing closer computational techniques and chemical research and making a remarkable leap toward harnessing AI's full capabilities in scientific discovery.
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