Poster
in
Workshop: AI for Accelerated Materials Design (AI4Mat-2023)
LLM Drug Discovery Challenge: A Contest as a Feasibility Study on the Utilization of Large Language Models in Medicinal Chemistry
Kusuri Murakumo · Naruki Yoshikawa · Kentaro Rikimaru · Shogo Nakamura · Kairi Furui · Takamasa Suzuki · Hiroyuki Yamasaki · Yuki Nishigaya · Yuzo Takagi · Masahito Ohue
Keywords: [ chemoinformatics ] [ Large language models ] [ Drug Discovery ] [ computational chemistry ] [ medicinal chemistry ] [ large language models ] [ drug discovery ]
The ultimate ideal in AI-driven drug discovery is the automatic design of specific drugs for individual diseases, yet this goal remains technically distant at present. However, recent advancements in large language models (LLMs) have significantly broadened the scope of applications with various tasks being explored in the chemistry domain. To probe the potential of utilizing LLMs in drug discovery, we organized a contest: the LLM Drug Discovery Challenge. Participants were tasked with proposing molecular structures of active compound candidates for a designated drug target using LLM-based workflows. The proposed chemical structures were evaluated comprehensively through scoring by a panel of five judges with deep expertise in medicinal chemistry, structural biology, and computational chemistry. Nine participants tackled the challenge with their unique methodologies, exploring the possibilities and current limitations of leveraging LLMs in drug discovery. In this rapidly advancing field, we aim to discuss the directions of future developments and what is expected moving forward.