ArticleDrugs2025
Aligning Large Language Models with Humans: A Comprehensive Survey of ChatGPT's Aptitude in Pharmacology.
Article in Drugs, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- A comparative study of the performance of different large language models in the Chinese National Pharmacist Licensing Examination.Frontiers in medicine · 2026Article
- A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains.NPJ digital medicine · 2025Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
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Authors and funding
14 authors.
Funding
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.