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Artificial intelligence in drug design

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Abstract

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (21210003 and 81230076 to H.J., 81773634 to M.Z. and 81430084 to K.C.), the “Personalized Medicines—Molecular Signature-based Drug Discovery and Development”, Strategic Priority Research Program of the Chinese Academy of Sciences (XDA12050201 to M.Z.), National Key Research & Development Plan (2016YFC1201003 to M.Z.), and the National Basic Research Program (2015CB910304 to X.L.).

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Authors and Affiliations

  1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China

    Feisheng Zhong, Jing Xing, Xutong Li, Xiaohong Liu, Zunyun Fu, Zhaoping Xiong, Dong Lu, Xiaolong Wu, Jihui Zhao, Xiaoqin Tan, Fei Li, Xiaomin Luo, Kaixian Chen, Mingyue Zheng & Hualiang Jiang

  2. School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China

    Feisheng Zhong, Jing Xing, Xutong Li, Zunyun Fu, Dong Lu, Xiaolong Wu, Jihui Zhao & Xiaoqin Tan

  3. School of Life Science and Technology, ShanghaiTech University, Shanghai, 200031, China

    Xiaohong Liu, Zhaoping Xiong, Kaixian Chen & Hualiang Jiang

  4. Department of Chemistry, College of Sciences, Shanghai University, Shanghai, 200444, China

    Fei Li

  5. School of Information Management, Dezhou University, Shandong, 253023, China

    Zhaojun Li

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  1. Feisheng Zhong
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Correspondence to Mingyue Zheng or Hualiang Jiang.

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Zhong, F., Xing, J., Li, X. et al. Artificial intelligence in drug design. Sci. China Life Sci. 61, 1191–1204 (2018). https://doi.org/10.1007/s11427-018-9342-2

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