药物发现
药物开发
临床试验
疾病
药品
药物试验
计算机科学
数据科学
计算生物学
医学
生物信息学
药理学
生物
病理
作者
Yizhen Zheng,Huan Yee Koh,Meng Yang,Li Li,Lauren T. May,Geoffrey I. Webb,Shirui Pan,George M. Church
出处
期刊:Cornell University - arXiv
日期:2024-09-05
被引量:4
标识
DOI:10.48550/arxiv.2409.04481
摘要
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development.
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