文献类型：期刊文献

中文题名：Artificial Intelligence in Pharmaceutical Sciences

作者：Mingkun Lu[1,3];Jiayi Yin[1];Qi Zhu[1];Gaole Lin[1];Minjie Mou[1];Fuyao Liu[1];Ziqi Pan[1];Nanxin You[1];Xichen Lian[1];Fengcheng Li[1];Hongning Zhang[1];Lingyan Zheng[1,3];Wei Zhang[1];Hanyu Zhang[1];Zihao Shen[2,4];Zhen Gu[1];Honglin Li[2,4,5];Feng Zhu[1,3]

机构：[1]College of Pharmaceutical Sciences&The Second Affiliated Hospital,School of Medicine,Zhejiang University,Hangzhou 310058,China;[2]Shanghai Key Laboratory of New Drug Design,East China University of Science and Technology,Shanghai 200237,China;[3]Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare,Hangzhou 330110,China;[4]Innovation Center for AI and Drug Discovery,East China Normal University,Shanghai 200062,China;[5]Lingang Laboratory,Shanghai 200031,China

年份：2023

期号：8

起止页码：37

中文期刊名：Engineering

外文期刊名：工程（英文）

收录：CSTPCD;;Scopus;CSCD:【CSCD2023_2024】；PubMed;

基金：funded by the Natural Science Foundation of Zhejiang Province(LR21H300001);National Key R&D Program of China(2022YFC3400501);National Natural Science Foundation of China(22220102001,U1909208,81872798,and 81825020);Leading Talent of the“Ten Thousand Plan”-National High-Level Talents Special Support Plan of China;Fundamental Research Fund of Central University(2018QNA7023);Key R&D Program of Zhejiang Province(2020C03010);“Double Top-Class”University(181201*194232101)。

语种：英文

中文关键词：Artificial intelligence;Machine learning;Deep learning;Target identification;Target discovery;Drug design;Drug discovery

摘要：Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.