A survey of current trends in computational drug repositioning

Brief Bioinform. 2016 Jan;17(1):2-12. doi: 10.1093/bib/bbv020. Epub 2015 Mar 31.


Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.

Keywords: chemical structure; computational drug repositioning; drug combination; genome; integrative strategies; phenome; prediction validation.

Publication types

  • Research Support, N.I.H., Intramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Computational Biology / trends
  • Data Mining
  • Drug Combinations
  • Drug Repositioning / statistics & numerical data
  • Drug Repositioning / trends*
  • Genomics
  • Humans
  • Machine Learning
  • Molecular Structure
  • Phenotype
  • Surveys and Questionnaires


  • Drug Combinations