A review of computational drug repositioning: Strategies, approaches, opportunities, challenges, and directions

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Jarada T. N., Rokne J. G., Alhajj R.

Journal of Cheminformatics, vol.12, no.1, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 12 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1186/s13321-020-00450-7
  • Journal Name: Journal of Cheminformatics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Biotechnology Research Abstracts, Directory of Open Access Journals
  • Keywords: Computational drug repositioning, Drug repositioning strategies, Data mining, Machine learning, Network analysis
  • Istanbul Medipol University Affiliated: Yes


Drug repositioning is the process of identifying novel therapeutic potentials for existing drugs and discovering therapies for untreated diseases. Drug repositioning, therefore, plays an important role in optimizing the pre-clinical process of developing novel drugs by saving time and cost compared to the traditional de novo drug discovery processes. Since drug repositioning relies on data for existing drugs and diseases the enormous growth of publicly available large-scale biological, biomedical, and electronic health-related data along with the high-performance computing capabilities have accelerated the development of computational drug repositioning approaches. Multidisciplinary researchers and scientists have carried out numerous attempts, with different degrees of efficiency and success, to computationally study the potential of repositioning drugs to identify alternative drug indications. This study reviews recent advancements in the field of computational drug repositioning. First, we highlight different drug repositioning strategies and provide an overview of frequently used resources. Second, we summarize computational approaches that are extensively used in drug repositioning studies. Third, we present different computing and experimental models to validate computational methods. Fourth, we address prospective opportunities, including a few target areas. Finally, we discuss challenges and limitations encountered in computational drug repositioning and conclude with an outline of further research directions.