WDGBANDTI: A Deep Graph Convolutional Network-Based Bilinear Attention Network for Drug-Target Interaction Prediction with Domain Adaptation.

Journal: Interdisciplinary sciences, computational life sciences
Published Date: May 23, 2025

Abstract

BACKGROUNDS: During the development of new drugs, it is essential to assess their effectiveness and examine the potential mechanisms behind side effects. This process typically involves combining the analysis of drugs under development with relevant existing drugs to more precisely evaluate the effects of drugs and targets. The use of deep learning methods to analyze this problem is currently a research hotspot, but several limitations remain: (i) how to deepen the analysis from the molecular level to the atomic level and analyze the key substructures that affect interactions on the basis of pharmaceutical mechanisms; (ii) how to integrate biomedical analysis with deep learning methods to make it medically sound and enhance interpretability.

Authors

  • Nianrui Wang
    School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China.
  • Shumin Zhao
    School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China.
  • Ziwei Li
    Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
  • Jianqiang Sun
    School of Automation and Electrical Engineering, Linyi University, Linyi, 276000, China.
  • Ming Yi
    School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China.

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