  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.
