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.
Bibliographical noteFunding Information:
This work was partly supported by the National Natural Science Foundation of China ( 61773196 , 32070681 ), Guangdong Provincial Special Projects on COVID-19 ( 2020KZDZX1182 ), Guangdong Provincial Key Laboratory Funds ( 2019B030301001 , 2017B030301018 ), Shenzhen Research Funds ( JCYJ20170817104740861 ), Shenzhen Peacock Plan ( KQTD2016053117035204 ), and by the Center for Computational Science and Engineering of Southern University of Science and Technology . The kind help of Prof. No's group is acknowledged.
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