MINDG: a drug-target interaction prediction method based on an integrated learning algorithm.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Drug-target interaction (DTI) prediction refers to the prediction of whether a given drug molecule will bind to a specific target and thus exert a targeted therapeutic effect. Although intelligent computational approaches for drug target prediction have received much attention and made many advances, they are still a challenging task that requires further research. The main challenges are manifested as follows: (i) most graph neural network-based methods only consider the information of the first-order neighboring nodes (drug and target) in the graph, without learning deeper and richer structural features from the higher-order neighboring nodes. (ii) Existing methods do not consider both the sequence and structural features of drugs and targets, and each method is independent of each other, and cannot combine the advantages of sequence and structural features to improve the interactive learning effect.

Authors

  • Hailong Yang
    School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China.
  • Yue Chen
    The College of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Yun Zuo
    Department of Mathematics, Dalian Maritime University, No. 1 Linghai Road, Dalian 116026, China.
  • Zhaohong Deng
    School of Digital Media, Jiangnan University, Wuxi, Jiangsu, China.
  • Xiaoyong Pan
    Department of Veterinary Clinical and Animal Sciences, University of Copenhagen, Copenhagen, Denmark. xypan172436@gmail.com.
  • Hong-Bin Shen
    Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China. hbshen@sjtu.edu.cn.
  • Kup-Sze Choi
    Centre for Smart Heath, School of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
  • Dong-Jun Yu