Utilizing Large Language Models for Natural Interface to Pharmacology Databases

TL;DR

This paper presents a Large Language Model-based natural language interface that enables pharmacologists to efficiently access and query complex pharmaceutical databases, streamlining various stages of drug development.

Contribution

The paper introduces a novel LLM-based framework for natural language querying of structured pharmacological data, demonstrating its generalizability and effectiveness.

Findings

Framework effectively queries diverse pharmaceutical databases

Demonstrates feasibility of LLM-based natural language interfaces in pharmacology

Shows potential to streamline drug development tasks

Abstract

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.