GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.

Journal: Briefings in bioinformatics
Published Date: Jan 17, 2022

Abstract

MOTIVATION: Identifying proteins that interact with drugs plays an important role in the initial period of developing drugs, which helps to reduce the development cost and time. Recent methods for predicting drug-protein interactions mainly focus on exploiting various data about drugs and proteins. These methods failed to completely learn and integrate the attribute information of a pair of drug and protein nodes and their attribute distribution.

Authors

  • Ping Xuan
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Mengsi Fan
    School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.
  • Hui Cui
    Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, 1278 Keyuan Road, Shanghai 201203, PR China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, PR China.
  • Tiangang Zhang
    School of Mathematical Science, Heilongjiang University, Harbin 150080, China. zhang@hlju.edu.cn.
  • Toshiya Nakaguchi

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