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

文献类型： 外文期刊

作者： Zhang, Yingbo 1 ; Ren, Shumin 1 ; Wang, Jiao 1 ; Lu, Junyu 1 ; Wu, Cong 1 ; He, Mengqiao 1 ; Liu, Xingyun 1 ; Wu, Rongrong 1 ; Zhao, Jing 1 ; Zhan, Chaoying 1 ; Du, Dan 5 ; Zhan, Zhajun 6 ; Singla, Rajeev K. 1 ; Shen, Bairong 1 ;

作者机构： 1.Sichuan Univ, Dept Pharm, Chengdu 610212, Peoples R China

2.Sichuan Univ, West China Hosp, Inst Syst Genet, Frontiers Sci Ctr Dis, Chengdu 610212, Peoples R China

3.Chinese Acad Trop Agr Sci CATAS, Trop Crops Genet Resources Inst, Haikou 571101, Peoples R China

4.Univ A Coruna, Dept Comp Sci & Informat Technol, La Coruna 15071, Spain

5.Sichuan Univ, West China Hosp, Adv Mass Spectrometry Ctr,Res Core Facil, Frontiers Sci Ctr Dis Related Mol Network, Chengdu 610041, Peoples R China

6.Zhejiang Univ Technol, Coll Pharmaceut Sci, Hangzhou 310014, Peoples R China

7.Lovely Profess Univ, Sch Pharmaceut Sci, Phagwara 144411, Punjab, India

期刊名称：DRUGS （ 影响因子：14.4； 五年影响因子：13.2 ）

ISSN： 0012-6667

年卷期： 2025 年 85 卷 2 期

页码：

收录情况： SCI

摘要： BackgroundDue 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.AimsThis study aims to provide a test set reference for assessing the application potential of both general-purpose and specialized LLMs in pharmacology.MethodsWe 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.ResultsThe 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 depth in knowledge integration and analysis limits its application in scientific research and clinical exploration.ConclusionTherefore, exploring retrieval-augmented generation (RAG) or integrating proprietary knowledge bases and knowledge graphs into pharmacology-oriented ChatGPT systems would yield favorable results. This integration will further optimize the potential of LLMs in pharmacology.

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