pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression

Autores
Goodarzi, Mohammad; Freitas, Matheus P.; Wu, Chih H.; Duchowicz, Pablo Román
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The pKa values of a series of 107 indicators have been modeled by means of a quantitative structure–property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/predictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The prediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitabledescriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher predictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences.
Fil: Goodarzi, Mohammad. Islamic Azad University; Irán
Fil: Freitas, Matheus P.. Universidad Federal de Lavras; Brasil
Fil: Wu, Chih H.. National Taichung University; China
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Materia
pKa
pH indicators
Quantitative structureproperty relationships
Support vector machines
GA-LSSVR
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/247639
Acceder
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network_name_str CONICET Digital (CONICET)
spelling pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regressionGoodarzi, MohammadFreitas, Matheus P.Wu, Chih H.Duchowicz, Pablo RománpKapH indicatorsQuantitative structureproperty relationshipsSupport vector machinesGA-LSSVRhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The pKa values of a series of 107 indicators have been modeled by means of a quantitative structure–property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/predictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The prediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitabledescriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher predictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences.Fil: Goodarzi, Mohammad. Islamic Azad University; IránFil: Freitas, Matheus P.. Universidad Federal de Lavras; BrasilFil: Wu, Chih H.. National Taichung University; ChinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaElsevier Science2010-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/247639Goodarzi, Mohammad; Freitas, Matheus P.; Wu, Chih H.; Duchowicz, Pablo Román; pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 101; 2; 4-2010; 102-1090169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743910000274info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2010.02.003info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:10:37Zoai:ri.conicet.gov.ar:11336/247639instacron: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:34982025-09-29 10:10:37.74CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
title pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
spellingShingle pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
Goodarzi, Mohammad
pKa
pH indicators
Quantitative structureproperty relationships
Support vector machines
GA-LSSVR
title_short pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
title_full pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
title_fullStr pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
title_full_unstemmed pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
title_sort pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
dc.creator.none.fl_str_mv Goodarzi, Mohammad
Freitas, Matheus P.
Wu, Chih H.
Duchowicz, Pablo Román
author Goodarzi, Mohammad
author_facet Goodarzi, Mohammad
Freitas, Matheus P.
Wu, Chih H.
Duchowicz, Pablo Román
author_role author
author2 Freitas, Matheus P.
Wu, Chih H.
Duchowicz, Pablo Román
author2_role author
author
author
dc.subject.none.fl_str_mv pKa
pH indicators
Quantitative structureproperty relationships
Support vector machines
GA-LSSVR
topic pKa
pH indicators
Quantitative structureproperty relationships
Support vector machines
GA-LSSVR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The pKa values of a series of 107 indicators have been modeled by means of a quantitative structure–property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/predictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The prediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitabledescriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher predictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences.
Fil: Goodarzi, Mohammad. Islamic Azad University; Irán
Fil: Freitas, Matheus P.. Universidad Federal de Lavras; Brasil
Fil: Wu, Chih H.. National Taichung University; China
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
description The pKa values of a series of 107 indicators have been modeled by means of a quantitative structure–property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/predictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The prediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitabledescriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher predictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences.
publishDate 2010
dc.date.none.fl_str_mv 2010-04
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/11336/247639
Goodarzi, Mohammad; Freitas, Matheus P.; Wu, Chih H.; Duchowicz, Pablo Román; pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 101; 2; 4-2010; 102-109
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/247639
identifier_str_mv Goodarzi, Mohammad; Freitas, Matheus P.; Wu, Chih H.; Duchowicz, Pablo Román; pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 101; 2; 4-2010; 102-109
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/url/https://www.sciencedirect.com/science/article/pii/S0169743910000274
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2010.02.003
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv 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 13.070432

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