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.

翻译：近年来，以ChatGPT为代表的对话式大型语言模型在药物发现等多个领域展现出显著的应用潜力。然而，现有研究主要聚焦于对话式大语言模型在化学反应和逆合成分析中的应用能力。作为药物研发管线中的关键任务，药物编辑仍处于探索空白。为填补这一研究空白，我们提出ChatDrug框架，系统性地探索利用大语言模型实现药物编辑的可行性。该框架联合运用提示模块、检索与领域反馈模块及对话模块，构建高效的药物编辑流程。实验结果表明，在涵盖小分子、多肽及蛋白质的39项药物编辑任务中，ChatDrug有33项任务取得最优性能。通过10项案例研究进一步证实，ChatDrug能够准确识别需修饰的关键子结构（包括分子官能团、多肽基序及蛋白质结构），生成多样且有效的药物编辑建议。值得注意的是，ChatDrug还能从领域专业视角提供具有洞察力的解释，显著增强模型可解释性并支持理性决策。本研究揭示了ChatGPT及对话式大语言模型在药物编辑领域的应用潜力，为构建更高效、协同的药物研发管线奠定基础，推动医药研发领域的创新发展。