Network-based Drug Repurposing: A Critical Review

Nagaraj Selvaraj, Akey Krishna Swaroop, Bala Sai Soujith Nidamanuri, Rajesh R. Kumar, Jawahar Natarajan, Jubie Selvaraj

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations


New drug development for a disease is a tedious, time-consuming, complex, and expensive process. Even if it is done, the chances for success of newly developed drugs are still very low. Modern reports state that repurposing the pre-existing drugs will have more efficient functioning than newly developed drugs. This repurposing process will save time, reduce expenses and provide more success rate. The only limitation for this repurposing is getting a desired pharmacological and characteristic parameter of various drugs from vast data about medications, their effects, and target mechanisms. This drawback can be avoided by introducing computational methods of analysis. This includes various network analysis types that use various biological processes and relationships with various drugs to simplify data interpretation. Some of the data sets now available in standard, and simplified forms include gene expression, drug-target interactions, protein networks, electronic health records, clinical trial results, and drug adverse event reports. Integrating various data sets and interpretation methods allows a more efficient and easy way to repur-pose an exact drug for the desired target and effect. In this review, we are going to discuss briefly various computational biological network analysis methods like gene regulatory networks, metabolic networks, protein-protein interaction networks, drug-target interaction networks, drug-disease association networks, drug-drug interaction networks, drug-side effects networks, integrated network-based methods, semantic link networks, and isoform-isoform networks. Along with this, we briefly discussed the drug's limitations, prediction methodologies, and data sets utilised in various biological networks for drug repurposing.

Original languageEnglish
Pages (from-to)116-131
Number of pages16
JournalCurrent Drug Research Reviews
Issue number2
StatePublished - Jul 2022
Externally publishedYes

Bibliographical note

Funding Information:
The work was supported by JSSAHER Research grant (Ref: REG/DIR (R)/URG/54/2011-12/5663, dated 1/10/2021).

Publisher Copyright:
© 2022 Bentham Science Publishers.


  • Drug repurposing
  • biological network analysis methods
  • data sets
  • drug development
  • network analysis
  • predicting methods


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