A review of SARS-CoV-2 drug repurposing: databases and machine learning models

Marim Elkashlan, Rahaf M. Ahmad, Malak Hajar, Fatma Al Jasmi, Juan Manuel Corchado, Nurul Athirah Nasarudin, Mohd Saberi Mohamad

Research output: Contribution to journalReview articlepeer-review

4 Citations (Scopus)

Abstract

The emergence of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) posed a serious worldwide threat and emphasized the urgency to find efficient solutions to combat the spread of the virus. Drug repurposing has attracted more attention than traditional approaches due to its potential for a time- and cost-effective discovery of new applications for the existing FDA-approved drugs. Given the reported success of machine learning (ML) in virtual drug screening, it is warranted as a promising approach to identify potential SARS-CoV-2 inhibitors. The implementation of ML in drug repurposing requires the presence of reliable digital databases for the extraction of the data of interest. Numerous databases archive research data from studies so that it can be used for different purposes. This article reviews two aspects: the frequently used databases in ML-based drug repurposing studies for SARS-CoV-2, and the recent ML models that have been developed for the prospective prediction of potential inhibitors against the new virus. Both types of ML models, Deep Learning models and conventional ML models, are reviewed in terms of introduction, methodology, and its recent applications in the prospective predictions of SARS-CoV-2 inhibitors. Furthermore, the features and limitations of the databases are provided to guide researchers in choosing suitable databases according to their research interests.

Original languageEnglish
Article number1182465
JournalFrontiers in Pharmacology
Volume14
DOIs
Publication statusPublished - 2023

Keywords

  • SARS-CoV-2
  • artificial intelligence
  • bioinformatics
  • computational approach
  • data science
  • databases
  • drug repurposing
  • machine learning

ASJC Scopus subject areas

  • Pharmacology
  • Pharmacology (medical)

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