人工智能在药学领域中的应用（英文）

作     者：Mingkun Lu Jiayi Yin Qi Zhu Gaole Lin Minjie Mou Fuyao Liu Ziqi Pan Nanxin You Xichen Lian Fengcheng Li Hongning Zhang Lingyan Zheng Wei Zhang Hanyu Zhang Zihao Shen Zhen Gu Honglin Li Feng Zhu 

作者机构：College of Pharmaceutical Sciences & The Second Affiliated HospitalSchool of MedicineZhejiang University Innovation Institute for Artificial Intelligence in Medicine of Zhejiang UniversityAlibaba–Zhejiang University Joint Research Center of Future Digital Healthcare Shanghai Key Laboratory of New Drug DesignEast China University of Science and Technology Innovation Center for AI and Drug DiscoveryEast China Normal University Lingang Laboratory 

出 版 物：《Engineering》 (工程(英文))

年 卷 期：2023年第8期

页      面：37-69页

核心收录：

学科分类：12[管理学] 1007[医学-药学(可授医学、理学学位)] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

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) 

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 *** 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.