ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain
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Shengchao Liu Jiongxiao Wang Yijin Yang Chengpeng Wang Ling Liu Hongyu Guo Chaowei Xiao
Recent advancements in conversational large language models (LLMs), such as
ChatGPT, have demonstrated remarkable promise in various domains, including
drug discovery. However, existing works mainly focus on investigating the
capabilities of conversational LLMs on chemical reaction and retrosynthesis.
While drug editing, a critical task in the drug discovery pipeline, remains
largely unexplored. To bridge this gap, we propose ChatDrug, a framework to
facilitate the systematic investigation of drug editing using LLMs. ChatDrug
jointly leverages a prompt module, a retrieval and domain feedback (ReDF)
module, and a conversation module to streamline effective drug editing. We
empirically show that ChatDrug reaches the best performance on 33 out of 39
drug editing tasks, encompassing small molecules, peptides, and proteins. We
further demonstrate, through 10 case studies, that ChatDrug can successfully
identify the key substructures (e.g., the molecule functional groups, peptide
motifs, and protein structures) for manipulation, generating diverse and valid
suggestions for drug editing. Promisingly, we also show that ChatDrug can offer
insightful explanations from a domain-specific perspective, enhancing
interpretability and enabling informed decision-making. This research sheds
light on the potential of ChatGPT and conversational LLMs for drug editing. It
paves the way for a more efficient and collaborative drug discovery pipeline,
contributing to the advancement of pharmaceutical research and development.