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

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. RESULTS: In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one…

• UD
  U.S. Department of Health and Human Services
• FN
  Frederick National Laboratory for Cancer Research
• NI
  National Institutes of Health

  Awards: K99HL138272, HHSN261200800001E, R00HL138272
• NH
  National Heart, Lung, and Blood Institute