• Lu Zhang, Jianjun Tan, Dan Han, and Hao Zhu.
• College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, 100124, China.
• Drug Discov. Today. 2017 Nov 1; 22 (11): 1680-1685.

AbstractMachine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.Copyright © 2017 Elsevier Ltd. All rights reserved.

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