文献类型：期刊文献

英文题名：Artificial Intelligence in Pharmaceutical Sciences

作者：Lu, Mingkun[1,2,4];Yin, Jiayi[1,2];Zhu, Qi[1,2];Lin, Gaole[1,2];Mou, Minjie[1,2];Liu, Fuyao[1,2];Pan, Ziqi[1,2];You, Nanxin[1,2];Lian, Xichen[1,2];Li, Fengcheng[1,2];Zhang, Hongning[1,2];Zheng, Lingyan[1,2,4];Zhang, Wei[1,2];Zhang, Hanyu[1,2];Shen, Zihao[3,5];Gu, Zhen[1,2];Li, Honglin[3,5,6];Zhu, Feng[1,2,4]

机构：[1]Zhejiang Univ, Sch Med, Coll Pharmaceut Sci, Hangzhou 310058, Peoples R China;[2]Zhejiang Univ, Sch Med, Affiliated Hosp 2, Hangzhou 310058, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Key Lab New Drug Design, Shanghai 200237, Peoples R China;[4]Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Alibaba Zhejiang Univ Joint Res Ctr Future Digita, Hangzhou 330110, Peoples R China;[5]East China Normal Univ, Innovat Ctr AI & Drug Discovery, Shanghai 200062, Peoples R China;[6]Lingang Lab, Shanghai 200031, Peoples R China

年份：2023

卷号：27

起止页码：37

外文期刊名：ENGINEERING

收录：;EI(收录号：20233814758916);WOS:【SCI-EXPANDED(收录号:WOS:001199163700001)】；

基金：This work was 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), Westlake Laboratory (Westlake Laboratory of Life Sciences and Biomedicine), Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, and Alibaba Cloud, Information Technology Center of Zhejiang University.

语种：英文

外文关键词：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. (c) 2023 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company.