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

Jiashun Mao, Javed Akhtar, Xiao Zhang, Liang Sun, Shenghui Guan, Xinyu Li, Guangming Chen, Jiaxin Liu, Hyeon Nae Jeon, Min Sung Kim, Kyoung Tai No, Guanyu Wang

Research output: Contribution to journalReview articlepeer-review

58 Citations (Scopus)


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.

Original languageEnglish
Article number103052
Issue number9
Publication statusPublished - 2021 Sept 24

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© 2021 The Author(s)

All Science Journal Classification (ASJC) codes

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