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Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
 
Creator Rajput, Akanksha
Thakur, Anamika
Mukhopadhyay, Adhip
Kamboj, Sakshi
Rastogi, Amber
Gautam, Sakshi
Jassal, Harvinder
Kumar, Manoj
 
Subject QR Microbiology
 
Description The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from ‘DrugRepV’ repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson’s correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These ‘anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.
 
Publisher Elsevier
 
Date 2021-05-24
 
Type Article
PeerReviewed
 
Relation https://www.sciencedirect.com/science/article/pii/S200103702100218X#:~:text=We%20found%20potential%20repurposed%20drugs,CoV%2D2%20and%20other%20Coronaviruses.
http://crdd.osdd.net/open/2724/
 
Identifier Rajput, Akanksha and Thakur, Anamika and Mukhopadhyay, Adhip and Kamboj, Sakshi and Rastogi, Amber and Gautam, Sakshi and Jassal, Harvinder and Kumar, Manoj (2021) Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning. COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 19. pp. 3133-3148.