A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target…

• NS
  National Science Foundation

  Award: CAREER
• PR
  Pharmaceutical Research and Manufacturers of America Foundation
• N
  Nvidia
• NN
  National Natural Science Foundation of China

  Awards: 81470839, 61472205
• TU
  Tsinghua University

  Award: 20161080086
• BA
  Beijing Advanced Innovation Center for Structural Biology, Tsinghua University