A deep learning method for drug-target affinity prediction based on sequence interaction information mining.

Journal: PeerJ
Published Date: Jan 1, 2023

Abstract

BACKGROUND: A critical aspect of drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches. In recent years, deep learning has emerged as a promising technique for DTA prediction, leveraging the substantial computational power of modern computers.

Authors

  • Mingjian Jiang
    College of Information Science and Engineering, Ocean University of China, Qingdao, China. jmj@stu.ouc.edu.cn.
  • Yunchang Shao
    School of Information and Control Engineering, Qingdao University of Technology, Qingdao, Shandong, China.
  • Yuanyuan Zhang
    National Clinical Research Center for Kidney Disease, State Key Laboratory for Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
  • Wei Zhou
    Department of Eye Function Laboratory, Eye Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
  • Shunpeng Pang
    School of Computer Engineering, WeiFang University, Weifang, Shandong, China.

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