Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories

Autores
Mercader, Andrew Gustavo; Duchowicz, Pablo Román
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The selection of an optimal set of molecular descriptors from a much larger collection of such regression variables is a vital step in the elaboration of most QSAR and QSPR models. The aim of this work is to continue advancing this important selection process by combining the enhanced replacement method (ERM) and the well-known genetic algorithms (GA). These approaches had previously proven to yield near-optimal results with a much smaller number of linear regressions than a full search. The newly proposed algorithms were tested on four different experimental datasets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that the new alternative ERMp (combination of ERM with a GA population) further improves ERM, it has previously been shown that the latter is superior to GA for the selection of an optimal set of molecular descriptors from a much greater pool.
Fil: Mercader, Andrew Gustavo. 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
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
Teoría Qspr-Qsar
Método del Reemplazo
Análisis de Regresión Lineal Multivariable
Técnica de Selección de Variables
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:11336/48699
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theoriesMercader, Andrew GustavoDuchowicz, Pablo RománTeoría Qspr-QsarMétodo del ReemplazoAnálisis de Regresión Lineal MultivariableTécnica de Selección de Variableshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The selection of an optimal set of molecular descriptors from a much larger collection of such regression variables is a vital step in the elaboration of most QSAR and QSPR models. The aim of this work is to continue advancing this important selection process by combining the enhanced replacement method (ERM) and the well-known genetic algorithms (GA). These approaches had previously proven to yield near-optimal results with a much smaller number of linear regressions than a full search. The newly proposed algorithms were tested on four different experimental datasets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that the new alternative ERMp (combination of ERM with a GA population) further improves ERM, it has previously been shown that the latter is superior to GA for the selection of an optimal set of molecular descriptors from a much greater pool.Fil: Mercader, Andrew Gustavo. 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; ArgentinaFil: 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 Science2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/48699Mercader, Andrew Gustavo; Duchowicz, Pablo Román; Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 149; 3-2015; 117-1220169-7439CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.chemolab.2015.10.007info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915002580info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:17Zoai:ri.conicet.gov.ar:11336/48699instacron: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:34982025-09-03 09:57:17.501CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
title Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
spellingShingle Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
Mercader, Andrew Gustavo
Teoría Qspr-Qsar
Método del Reemplazo
Análisis de Regresión Lineal Multivariable
Técnica de Selección de Variables
title_short Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
title_full Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
title_fullStr Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
title_full_unstemmed Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
title_sort Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories
dc.creator.none.fl_str_mv Mercader, Andrew Gustavo
Duchowicz, Pablo Román
author Mercader, Andrew Gustavo
author_facet Mercader, Andrew Gustavo
Duchowicz, Pablo Román
author_role author
author2 Duchowicz, Pablo Román
author2_role author
dc.subject.none.fl_str_mv Teoría Qspr-Qsar
Método del Reemplazo
Análisis de Regresión Lineal Multivariable
Técnica de Selección de Variables
topic Teoría Qspr-Qsar
Método del Reemplazo
Análisis de Regresión Lineal Multivariable
Técnica de Selección de Variables
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 selection of an optimal set of molecular descriptors from a much larger collection of such regression variables is a vital step in the elaboration of most QSAR and QSPR models. The aim of this work is to continue advancing this important selection process by combining the enhanced replacement method (ERM) and the well-known genetic algorithms (GA). These approaches had previously proven to yield near-optimal results with a much smaller number of linear regressions than a full search. The newly proposed algorithms were tested on four different experimental datasets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that the new alternative ERMp (combination of ERM with a GA population) further improves ERM, it has previously been shown that the latter is superior to GA for the selection of an optimal set of molecular descriptors from a much greater pool.
Fil: Mercader, Andrew Gustavo. 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
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 selection of an optimal set of molecular descriptors from a much larger collection of such regression variables is a vital step in the elaboration of most QSAR and QSPR models. The aim of this work is to continue advancing this important selection process by combining the enhanced replacement method (ERM) and the well-known genetic algorithms (GA). These approaches had previously proven to yield near-optimal results with a much smaller number of linear regressions than a full search. The newly proposed algorithms were tested on four different experimental datasets, formed by collections of 116, 200, 78, and 100 experimental records from different compounds and 1268, 1338, 1187, and 1306 molecular descriptors, respectively. The comparisons showed that the new alternative ERMp (combination of ERM with a GA population) further improves ERM, it has previously been shown that the latter is superior to GA for the selection of an optimal set of molecular descriptors from a much greater pool.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
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/11336/48699
Mercader, Andrew Gustavo; Duchowicz, Pablo Román; Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 149; 3-2015; 117-122
0169-7439
CONICET Digital
CONICET
url http://hdl.handle.net/11336/48699
identifier_str_mv Mercader, Andrew Gustavo; Duchowicz, Pablo Román; Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories; Elsevier Science; Chemometrics and Intelligent Laboratory Systems; 149; 3-2015; 117-122
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.2015.10.007
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0169743915002580
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
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 13.13397

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