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

Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Elkashlan, Marim;
Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Ahmad, Rahaf M.;
Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Hajar, Malak;
Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Al Jasmi, Fatma;
Affiliation
Departamento de Informática y Automática ,Facultad de Ciencias ,Grupo de Investigación BISITE ,Instituto de Investigación Biomédica de Salamanca ,University of Salamanca ,Salamanca ,Spain
Corchado, Juan Manuel;
Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Nasarudin, Nurul Athirah;
Affiliation
Health Data Science Lab ,Department of Genetics and Genomics ,College of Medical and Health Sciences ,United Arab Emirates University ,Al Ain ,United Arab Emirates
Mohamad, Mohd Saberi

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.

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License Holder: Copyright © 2023 Elkashlan, Ahmad, Hajar, Al Jasmi, Corchado, Nasarudin and Mohamad.

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