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SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-...

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Autores principales: Ahmed, Faheem, Lee, Jae Wook, Samantasinghar, Anupama, Kim, Young Su, Kim, Kyung Hwan, Kang, In Suk, Memon, Fida Hussain, Lim, Jong Hwan, Choi, Kyung Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244710/
https://www.ncbi.nlm.nih.gov/pubmed/35784208
http://dx.doi.org/10.3389/fpubh.2022.902123
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author Ahmed, Faheem
Lee, Jae Wook
Samantasinghar, Anupama
Kim, Young Su
Kim, Kyung Hwan
Kang, In Suk
Memon, Fida Hussain
Lim, Jong Hwan
Choi, Kyung Hyun
author_facet Ahmed, Faheem
Lee, Jae Wook
Samantasinghar, Anupama
Kim, Young Su
Kim, Kyung Hwan
Kang, In Suk
Memon, Fida Hussain
Lim, Jong Hwan
Choi, Kyung Hyun
author_sort Ahmed, Faheem
collection PubMed
description The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).
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spelling pubmed-92447102022-07-01 SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19 Ahmed, Faheem Lee, Jae Wook Samantasinghar, Anupama Kim, Young Su Kim, Kyung Hwan Kang, In Suk Memon, Fida Hussain Lim, Jong Hwan Choi, Kyung Hyun Front Public Health Public Health The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics). Frontiers Media S.A. 2022-06-16 /pmc/articles/PMC9244710/ /pubmed/35784208 http://dx.doi.org/10.3389/fpubh.2022.902123 Text en Copyright © 2022 Ahmed, Lee, Samantasinghar, Kim, Kim, Kang, Memon, Lim and Choi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Ahmed, Faheem
Lee, Jae Wook
Samantasinghar, Anupama
Kim, Young Su
Kim, Kyung Hwan
Kang, In Suk
Memon, Fida Hussain
Lim, Jong Hwan
Choi, Kyung Hyun
SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title_full SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title_fullStr SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title_full_unstemmed SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title_short SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19
title_sort speropredictor: an integrated machine learning and molecular docking-based drug repurposing framework with use case of covid-19
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9244710/
https://www.ncbi.nlm.nih.gov/pubmed/35784208
http://dx.doi.org/10.3389/fpubh.2022.902123
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