deepDR: a network-based deep learning approach to in silico drug repositioning.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging.

Authors

  • Xiangxiang Zeng
    Department of Computer Science, Hunan University, Changsha, China.
  • Siyi Zhu
    Department of Computer Science, Xiamen University, Xiamen 361005, China.
  • Xiangrong Liu
  • Yadi Zhou
    Department of Chemistry and Biochemistry , Ohio University , Athens , Ohio 45701 , United States.
  • Ruth Nussinov
    Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA.
  • Feixiong Cheng
    Genomic Medicine Institute, Lerner Research Institute , Cleveland Clinic , Cleveland , Ohio 44106 , United States.