A compact review of progress and prospects of deep learning in drug discovery.

Journal: Journal of molecular modeling
Published Date: Mar 28, 2023

Abstract

BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development.

Authors

  • Huijun Li
  • Lin Zou
    College of Medicine, Guangxi University, Nanning, 530004, China.
  • Jamal Alzobair Hammad Kowah
    College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China.
  • Dongqiong He
    College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China.
  • Zifan Liu
    College of Medicine, Guangxi University, Nanning, 530004, China.
  • Xuejie Ding
    College of Medicine, Guangxi University, Nanning, 530004, China.
  • Hao Wen
  • Lisheng Wang
    Department of Automation, Shanghai Jiaotong University, China.
  • Mingqing Yuan
    Medical College of Guangxi University, Nanning, Guangxi,China.
  • Xu Liu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuxu16@bjut.edu.cn.

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