A review of network-based approaches to drug repositioning

Maryam Lotfi Shahreza, Nasser Ghadiri, Seyed Rasoul Mousavi, Jaleh Varshosaz, James R. Green

Research output: Contribution to journalArticlepeer-review

189 Citations (Scopus)


Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.

We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.
Original languageEnglish
Pages (from-to)878-892
Number of pages15
JournalBriefings in Bioinformatics
Issue number5
Early online date27 Feb 2017
Publication statusPublished - Sept 2018
Externally publishedYes


  • Drug repurposing
  • Drug–target interaction
  • Biological networks
  • Network analysis
  • Machine learning


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