Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors

Autores
Andrada, Matias Fernando; Vega Hissi, Esteban Gabriel; Estrada, Mario Rinaldo; Garro Martinez, Juan Ceferino
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that piID (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are Rtrain=0.811 and Rtest=0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features.
Fil: Andrada, Matias Fernando. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Vega Hissi, Esteban Gabriel. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Estrada, Mario Rinaldo. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina
Fil: Garro Martinez, Juan Ceferino. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
5-Lipoxygenase Inhibitors
K-Means Clustering
Linear Discriminant Analysis
Multivariate Linear Regression
Qsar
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/60452
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitorsAndrada, Matias FernandoVega Hissi, Esteban GabrielEstrada, Mario RinaldoGarro Martinez, Juan Ceferino5-Lipoxygenase InhibitorsK-Means ClusteringLinear Discriminant AnalysisMultivariate Linear RegressionQsarhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that piID (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are Rtrain=0.811 and Rtest=0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features.Fil: Andrada, Matias Fernando. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Vega Hissi, Esteban Gabriel. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Estrada, Mario Rinaldo. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; ArgentinaFil: Garro Martinez, Juan Ceferino. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier Science2015-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/60452Andrada, Matias Fernando; Vega Hissi, Esteban Gabriel; Estrada, Mario Rinaldo; Garro Martinez, Juan Ceferino; Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 143; 4-2015; 122-1290169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2015.03.001info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915000593info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:58:00Zoai:ri.conicet.gov.ar:11336/60452instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:58:00.349CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
title Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
spellingShingle Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
Andrada, Matias Fernando
5-Lipoxygenase Inhibitors
K-Means Clustering
Linear Discriminant Analysis
Multivariate Linear Regression
Qsar
title_short Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
title_full Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
title_fullStr Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
title_full_unstemmed Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
title_sort Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors
dc.creator.none.fl_str_mv Andrada, Matias Fernando
Vega Hissi, Esteban Gabriel
Estrada, Mario Rinaldo
Garro Martinez, Juan Ceferino
author Andrada, Matias Fernando
author_facet Andrada, Matias Fernando
Vega Hissi, Esteban Gabriel
Estrada, Mario Rinaldo
Garro Martinez, Juan Ceferino
author_role author
author2 Vega Hissi, Esteban Gabriel
Estrada, Mario Rinaldo
Garro Martinez, Juan Ceferino
author2_role author
author
author
dc.subject.none.fl_str_mv 5-Lipoxygenase Inhibitors
K-Means Clustering
Linear Discriminant Analysis
Multivariate Linear Regression
Qsar
topic 5-Lipoxygenase Inhibitors
K-Means Clustering
Linear Discriminant Analysis
Multivariate Linear Regression
Qsar
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that piID (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are Rtrain=0.811 and Rtest=0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features.
Fil: Andrada, Matias Fernando. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Vega Hissi, Esteban Gabriel. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Estrada, Mario Rinaldo. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina
Fil: Garro Martinez, Juan Ceferino. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description In this work, we performed a quantitative structure activity relationship (QSAR) model for a family of 5-lipoxygenase (5-LOX) inhibitors using k-means clustering and linear discriminant analysis (LDA) for the selection of training and test sets and multivariate linear regression (MLR) for the independent variable selection. With the k-means clustering method, the total set of compounds (58 derivatives of 5-Benzylidene-2-phenylthiazolinones) was divided in two clusters according to a simple discriminant function. We found that piID (conventional bond order ID number) molecular descriptor discriminates correctly 100% of the compounds of each clusters. Thirty different models divided in three series were analyzed and the series with representative training and test sets (series 3) had the most predictive models. The statistical parameters of the best model are Rtrain=0.811 and Rtest=0.801. We found that a rational selection in the setting-up of training and test sets allows to obtain the most predictive models and the random selection is sometimes unsuitable, especially, when the total set of compounds can be classified in different clusters according to structural features.
publishDate 2015
dc.date.none.fl_str_mv 2015-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/60452
Andrada, Matias Fernando; Vega Hissi, Esteban Gabriel; Estrada, Mario Rinaldo; Garro Martinez, Juan Ceferino; Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 143; 4-2015; 122-129
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/60452
identifier_str_mv Andrada, Matias Fernando; Vega Hissi, Esteban Gabriel; Estrada, Mario Rinaldo; Garro Martinez, Juan Ceferino; Application of k-means clustering, linear discriminant analysis and multivariate linear regression for the development of a predictive QSAR model on 5-lipoxygenase inhibitors; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 143; 4-2015; 122-129
0169-7439
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2015.03.001
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915000593
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.24909

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