Utilizing Large Language Models for Natural Interface to Pharmacology
  Databases
Hong Lu Chuan Li Yinheng Li Jie Zhao
The drug development process necessitates that pharmacologists undertake
various tasks, such as reviewing literature, formulating hypotheses, designing
experiments, and interpreting results. Each stage requires accessing and
querying vast amounts of information. In this abstract, we introduce a Large
Language Model (LLM)-based Natural Language Interface designed to interact with
structured information stored in databases. Our experiments demonstrate the
feasibility and effectiveness of the proposed framework. This framework can
generalize to query a wide range of pharmaceutical data and knowledge bases.