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大语言模型及其在药物发现与开发中的应用:入门指南。

Large Language Models and Their Applications in Drug Discovery and Development: A Primer.

作者信息

Lu James, Choi Keunwoo, Eremeev Maksim, Gobburu Jogarao, Goswami Srijib, Liu Qi, Mo Gary, Musante Cynthia J, Shahin Mohamed H

机构信息

Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA.

Prescient Design, Genentech Inc., South San Francisco, California, USA.

出版信息

Clin Transl Sci. 2025 Apr;18(4):e70205. doi: 10.1111/cts.70205.


DOI:10.1111/cts.70205
PMID:40208836
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11984503/
Abstract

Large language models (LLMs) have emerged as powerful tools in many fields, including clinical pharmacology and translational medicine. This paper aims to provide a comprehensive primer on the applications of LLMs to these disciplines. We will explore the fundamental concepts of LLMs, their potential applications in drug discovery and development processes ranging from facilitating target identification to aiding preclinical research and clinical trial analysis, and practical use cases such as assisting with medical writing and accelerating analytical workflows in quantitative clinical pharmacology. By the end of this paper, clinical pharmacologists and translational scientists will have a clearer understanding of how to leverage LLMs to enhance their research and development efforts.

摘要

大语言模型(LLMs)已成为包括临床药理学和转化医学在内的许多领域的强大工具。本文旨在全面介绍大语言模型在这些学科中的应用。我们将探讨大语言模型的基本概念,它们在药物发现和开发过程中的潜在应用,从促进靶点识别到协助临床前研究和临床试验分析,以及实际应用案例,如协助医学写作和加速定量临床药理学中的分析工作流程。在本文结尾,临床药理学家和转化科学家将更清楚地了解如何利用大语言模型来加强他们的研发工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/972571d884ff/CTS-18-e70205-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/726b930d8a30/CTS-18-e70205-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/a5aa63575756/CTS-18-e70205-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/972571d884ff/CTS-18-e70205-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/726b930d8a30/CTS-18-e70205-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/a5aa63575756/CTS-18-e70205-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4e3/11984503/972571d884ff/CTS-18-e70205-g003.jpg

相似文献

[1]
Large Language Models and Their Applications in Drug Discovery and Development: A Primer.

Clin Transl Sci. 2025-4

[2]
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[3]
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CPT Pharmacometrics Syst Pharmacol. 2020-12

[4]
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Clin Pharmacol Ther. 2020-3-3

[5]
AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.

J Adv Res. 2025-2-12

[6]
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Expert Rev Clin Pharmacol. 2024-4

[7]
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Clin Transl Sci. 2025-3

[8]
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Br J Clin Pharmacol. 2024-3

[9]
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Therap Adv Gastroenterol. 2024-2-22

[10]
Accelerating drug discovery, development, and clinical trials by artificial intelligence.

Med. 2024-9-13

本文引用的文献

[1]
Toward expert-level medical question answering with large language models.

Nat Med. 2025-3

[2]
Empowering biomedical discovery with AI agents.

Cell. 2024-10-31

[3]
Named entity recognition of pharmacokinetic parameters in the scientific literature.

Sci Rep. 2024-10-8

[4]
Bioinformatics and biomedical informatics with ChatGPT: Year one review.

Quant Biol. 2024-12

[5]
A scoping review of large language model based approaches for information extraction from radiology reports.

NPJ Digit Med. 2024-8-24

[6]
Evaluating the effectiveness of large language models in abstract screening: a comparative analysis.

Syst Rev. 2024-8-21

[7]
Fine-tuning large language models for chemical text mining.

Chem Sci. 2024-6-7

[8]
Performance of a Large Language Model in Screening Citations.

JAMA Netw Open. 2024-7-1

[9]
Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses.

J Med Internet Res. 2024-6-25

[10]
The Use of Generative AI for Scientific Literature Searches for Systematic Reviews: ChatGPT and Microsoft Bing AI Performance Evaluation.

JMIR Med Inform. 2024-5-14

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