Aligning Large Language Models with Humans: A Comprehensive Survey of ChatGPT’s Aptitude in Pharmacology

作者：Yingbo Zhang, Shumin Ren, Jiao Wang, Junyu Lu, Cong Wu, Mengqiao He, Xingyun Liu, Rongrong Wu, J. Y. Zhao, Chaoying Zhan, Dan Du, Zha‐Jun Zhan, Rajeev Kumar Singla, Bairong Shen · 发表于：Drugs · 年份：2024 · DOI：10.1007/s40265-024-02124-2 · 被引用次数：13 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、Computational Drug Discovery Methods

BACKGROUND: Due to the lack of a comprehensive pharmacology test set, evaluating the potential and value of large language models (LLMs) in pharmacology is complex and challenging. AIMS: This study aims to provide a test set reference for assessing the application potential of both general-purpose and specialized LLMs in pharmacology. METHODS: We constructed a pharmacology test set consisting of three tasks: drug information retrieval, lead compound structure optimization, and research trend summarization and analysis. Subsequently, we compared the performance of general-purpose LLMs GPT-3.5 and GPT-4 on this test set. RESULTS: The results indicate that GPT-3.5 and GPT-4 can better understand instructions for information retrieval, scheme optimization, and trend summarization in pharmacology, showing significant potential in basic pharmacology tasks, especially in areas such as drug pharmacological properties, pharmacokinetics, mode of action, and toxicity prediction. These general LLMs also effectively summarize the current challenges and future trends in this field, proving their valuable resource for interdisciplinary pharmacology researchers. However, the limitations of ChatGPT become evident when handling tasks such as drug identification queries, drug interaction information retrieval, and drug structure simulation optimization. It struggles to provide accurate interaction information for individual or specific drugs and cannot optimize specific drugs. This lack of dept...