Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models

Mao, Jiashun1,2,3; Akhtar, Javed2,4; Zhang, Xiao5; Sun, Liang6; Guan, Shenghui2,3; Li, Xinyu7,8; Chen, Guangming2,4; Liu, Jiaxin9; Jeon, Hyeon-Nae9; Kim, Min Sung9; No, Kyoung Tai1; Wang, Guanyu2,3,4

2021-09-24

10.1016/j.isci.2021.103052

ISCIENCE   影响因子和分区

2589-0042

Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.

SCI

英语

通讯

National Natural Science Foundation of China[61773196,32070681] ; Guangdong Provincial Special Projects[2020KZDZX1182] ; Guangdong Provincial Key Laboratory Funds["2019B030301001","2017B030301018"] ; Shenzhen Research Funds[JCYJ20170817104740861] ; Shenzhen Peacock Plan[KQTD2016053117035204]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:000698069100111

CELL PRESS

Web of Science

1.Yonsei Univ, Interdisciplinary Grad Program Integrat Biotechno, Incheon 21983, South Korea
2.Southern Univ Sci & Technol, Sch Life Sci, Dept Biol, 1088 Xueyuan Ave, Shenzhen 518055, Guangdong, Peoples R China
3.Guangdong Prov Key Lab Computat Sci & Mat Design, Shenzhen 518055, Guangdong, Peoples R China
4.Guangdong Prov Key Lab Cell Microenvironm & Dis R, Shenzhen 518055, Guangdong, Peoples R China
5.Shanghai Rural Commercial Bank Co Ltd, Shanghai 200002, Peoples R China
6.City Univ Hong Kong, Dept Phys, Kowloon, 83 Tat Chee Ave, Hong Kong, Peoples R China
7.Chinese Univ Hong Kong, Sch Life & Hlth Sci, Shenzhen 518172, Peoples R China
8.Chinese Univ Hong Kong, Warshel Inst Computat Biol, Shenzhen 518172, Peoples R China
9.Yonsei Univ, Coll Life Sci & Biotechnol, Biotechnol, Seoul 03722, South Korea

Mao, Jiashun,Akhtar, Javed,Zhang, Xiao,et al. Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models[J]. ISCIENCE,2021,24(9).

Mao, Jiashun.,Akhtar, Javed.,Zhang, Xiao.,Sun, Liang.,Guan, Shenghui.,...&Wang, Guanyu.(2021).Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models.ISCIENCE,24(9).

Mao, Jiashun,et al."Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models".ISCIENCE 24.9(2021).