Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.

Journal: BMC bioinformatics
Published Date: Jun 10, 2022

Abstract

BACKGROUND: Drug-Drug interactions (DDIs) are a challenging problem in drug research. Drug combination therapy is an effective solution to treat diseases, but it can also cause serious side effects. Therefore, DDIs prediction is critical in pharmacology. Recently, researchers have been using deep learning techniques to predict DDIs. However, these methods only consider single information of the drug and have shortcomings in robustness and scalability.

Authors

  • Changxiang He
    College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Yuru Liu
    College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Yaping Mao
    School of Mathematics and Statistis, Qinghai Normal University, Xining, 810008, China.
  • Xiaofei Qin
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
  • Lele Liu
    College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China. ahhylau@outlook.com.
  • Xuedian Zhang
    School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.

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