Cargando…

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

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 ha...

Descripción completa

Detalles Bibliográficos
Autores principales: Rajput, Akanksha, Thakur, Anamika, Mukhopadhyay, Adhip, Kamboj, Sakshi, Rastogi, Amber, Gautam, Sakshi, Jassal, Harvinder, Kumar, Manoj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141697/
https://www.ncbi.nlm.nih.gov/pubmed/34055238
http://dx.doi.org/10.1016/j.csbj.2021.05.037
_version_ 1783696418964242432
author Rajput, Akanksha
Thakur, Anamika
Mukhopadhyay, Adhip
Kamboj, Sakshi
Rastogi, Amber
Gautam, Sakshi
Jassal, Harvinder
Kumar, Manoj
author_facet Rajput, Akanksha
Thakur, Anamika
Mukhopadhyay, Adhip
Kamboj, Sakshi
Rastogi, Amber
Gautam, Sakshi
Jassal, Harvinder
Kumar, Manoj
author_sort Rajput, Akanksha
collection PubMed
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 (IC(50)/EC(50)) 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.
format Online
Article
Text
id pubmed-8141697
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Research Network of Computational and Structural Biotechnology
record_format MEDLINE/PubMed
spelling pubmed-81416972021-05-24 Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning Rajput, Akanksha Thakur, Anamika Mukhopadhyay, Adhip Kamboj, Sakshi Rastogi, Amber Gautam, Sakshi Jassal, Harvinder Kumar, Manoj Comput Struct Biotechnol J Research Article 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 (IC(50)/EC(50)) 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. Research Network of Computational and Structural Biotechnology 2021-05-24 /pmc/articles/PMC8141697/ /pubmed/34055238 http://dx.doi.org/10.1016/j.csbj.2021.05.037 Text en © 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Rajput, Akanksha
Thakur, Anamika
Mukhopadhyay, Adhip
Kamboj, Sakshi
Rastogi, Amber
Gautam, Sakshi
Jassal, Harvinder
Kumar, Manoj
Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title_full Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title_fullStr Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title_full_unstemmed Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title_short Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning
title_sort prediction of repurposed drugs for coronaviruses using artificial intelligence and machine learning
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8141697/
https://www.ncbi.nlm.nih.gov/pubmed/34055238
http://dx.doi.org/10.1016/j.csbj.2021.05.037
work_keys_str_mv AT rajputakanksha predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT thakuranamika predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT mukhopadhyayadhip predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT kambojsakshi predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT rastogiamber predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT gautamsakshi predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT jassalharvinder predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning
AT kumarmanoj predictionofrepurposeddrugsforcoronavirusesusingartificialintelligenceandmachinelearning