An Autonomous Large Language Model Agent for Chemical Literature Data
  Mining
Kexin Chen Hanqun Cao Junyou Li Yuyang Du Menghao Guo Xin Zeng Lanqing Li Jiezhong Qiu Pheng Ann Heng Guangyong Chen
Chemical synthesis, which is crucial for advancing material synthesis and
drug discovery, impacts various sectors including environmental science and
healthcare. The rise of technology in chemistry has generated extensive
chemical data, challenging researchers to discern patterns and refine synthesis
processes. Artificial intelligence (AI) helps by analyzing data to optimize
synthesis and increase yields. However, AI faces challenges in processing
literature data due to the unstructured format and diverse writing style of
chemical literature. To overcome these difficulties, we introduce an end-to-end
AI agent framework capable of high-fidelity extraction from extensive chemical
literature. This AI agent employs large language models (LLMs) for prompt
generation and iterative optimization. It functions as a chemistry assistant,
automating data collection and analysis, thereby saving manpower and enhancing
performance. Our framework's efficacy is evaluated using accuracy, recall, and
F1 score of reaction condition data, and we compared our method with human
experts in terms of content correctness and time efficiency. The proposed
approach marks a significant advancement in automating chemical literature
extraction and demonstrates the potential for AI to revolutionize data
management and utilization in chemistry.