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Recent advancements in conversational large language models (LLMs), such asChatGPT, have demonstrated remarkable promise in various domains, includingdrug discovery. However, existing works mainly focus on investigating thecapabilities of conversational LLMs on chemical reaction and retrosynthesis.While drug editing, a critical task in the drug discovery pipeline, remainslargely unexplored. To bridge this gap, we propose ChatDrug, a framework tofacilitate the systematic investigation of drug editing using LLMs. ChatDrugjointly leverages a prompt module, a retrieval and domain feedback (ReDF)module, and a conversation module to streamline effective drug editing. Weempirically show that ChatDrug reaches the best performance on 33 out of 39drug editing tasks, encompassing small molecules, peptides, and proteins. Wefurther demonstrate, through 10 case studies, that ChatDrug can successfullyidentify the key substructures (e.g., the molecule functional groups, peptidemotifs, and protein structures) for manipulation, generating diverse and validsuggestions for drug editing. Promisingly, we also show that ChatDrug can offerinsightful explanations from a domain-specific perspective, enhancinginterpretability and enabling informed decision-making. This research shedslight on the potential of ChatGPT and conversational LLMs for drug editing. Itpaves the way for a more efficient and collaborative drug discovery pipeline,contributing to the advancement of pharmaceutical research and development.

ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback