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Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) mod...

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Autores principales: Suay-Garcia, Beatriz, Falcó, Antonio, Bueso-Bordils, J. Ignacio, Anton-Fos, Gerardo M., Pérez-Gracia, M. Teresa, Alemán-López, Pedro A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7760995/
https://www.ncbi.nlm.nih.gov/pubmed/33260726
http://dx.doi.org/10.3390/ph13120431
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author Suay-Garcia, Beatriz
Falcó, Antonio
Bueso-Bordils, J. Ignacio
Anton-Fos, Gerardo M.
Pérez-Gracia, M. Teresa
Alemán-López, Pedro A.
author_facet Suay-Garcia, Beatriz
Falcó, Antonio
Bueso-Bordils, J. Ignacio
Anton-Fos, Gerardo M.
Pérez-Gracia, M. Teresa
Alemán-López, Pedro A.
author_sort Suay-Garcia, Beatriz
collection PubMed
description Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.
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spelling pubmed-77609952020-12-26 Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli Suay-Garcia, Beatriz Falcó, Antonio Bueso-Bordils, J. Ignacio Anton-Fos, Gerardo M. Pérez-Gracia, M. Teresa Alemán-López, Pedro A. Pharmaceuticals (Basel) Article Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli. MDPI 2020-11-28 /pmc/articles/PMC7760995/ /pubmed/33260726 http://dx.doi.org/10.3390/ph13120431 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Suay-Garcia, Beatriz
Falcó, Antonio
Bueso-Bordils, J. Ignacio
Anton-Fos, Gerardo M.
Pérez-Gracia, M. Teresa
Alemán-López, Pedro A.
Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title_full Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title_fullStr Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title_full_unstemmed Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title_short Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds against Escherichia coli
title_sort tree-based qsar model for drug repurposing in the discovery of new antibacterial compounds against escherichia coli
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7760995/
https://www.ncbi.nlm.nih.gov/pubmed/33260726
http://dx.doi.org/10.3390/ph13120431
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