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Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

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A Correction to this article was published on 25 June 2018

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Abstract

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

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Change history

  • 25 June 2018

    The name of the corresponding author should be ‘Xiang-Qun Xie’, rather than ‘Xiang-Qun Sean Xie’.

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Acknowledgements

The authors thank Dr. Yuanqiang Wang, Nan Wu, and Yubin Ge in the CCGS Center at the University of Pittsburgh (Pitt) for carefully reviewing the manuscripts and providing helpful comments for revision. Thanks to all the students and faculty in the CDAR Center, School of Pharmacy at Pitt for their help and supports. The authors also acknowledge the funding support to our laboratory from NIH NIDA (P30DA035778) and DOD (W81XWH-16-1-0490).

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Authors and Affiliations

  1. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA

    Yankang Jing, Yuemin Bian, Ziheng Hu, Lirong Wang & Xiang-Qun Sean Xie

  2. NIH National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yankang Jing, Yuemin Bian, Ziheng Hu, Lirong Wang & Xiang-Qun Sean Xie

  3. Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Yankang Jing, Yuemin Bian, Ziheng Hu, Lirong Wang & Xiang-Qun Sean Xie

  4. Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, 15261, USA

    Xiang-Qun Sean Xie

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Jing, Y., Bian, Y., Hu, Z. et al. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era. AAPS J 20, 58 (2018). https://doi.org/10.1208/s12248-018-0210-0

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