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ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19
Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607653/ https://www.ncbi.nlm.nih.gov/pubmed/36298508 http://dx.doi.org/10.3390/vaccines10101643 |
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author | Saha, Sovan Chatterjee, Piyali Halder, Anup Kumar Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz |
author_facet | Saha, Sovan Chatterjee, Piyali Halder, Anup Kumar Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz |
author_sort | Saha, Sovan |
collection | PubMed |
description | Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study. |
format | Online Article Text |
id | pubmed-9607653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96076532022-10-28 ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 Saha, Sovan Chatterjee, Piyali Halder, Anup Kumar Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz Vaccines (Basel) Article Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study. MDPI 2022-09-30 /pmc/articles/PMC9607653/ /pubmed/36298508 http://dx.doi.org/10.3390/vaccines10101643 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Saha, Sovan Chatterjee, Piyali Halder, Anup Kumar Nasipuri, Mita Basu, Subhadip Plewczynski, Dariusz ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title | ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title_full | ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title_fullStr | ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title_full_unstemmed | ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title_short | ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19 |
title_sort | ml-dtd: machine learning-based drug target discovery for the potential treatment of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607653/ https://www.ncbi.nlm.nih.gov/pubmed/36298508 http://dx.doi.org/10.3390/vaccines10101643 |
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