Adaptive matrix metrics for molecular descriptor assessment in QSPR classification

Autores
Soto, Axel Juan; Strickert, Marc; Vazquez, Gustavo Esteban
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
QSPR methods represent a useful approach in the drug discovery process, since they allow to predict in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions. In this work, a matrix-based method is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy. A recently proposed method making use of this concept is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property shows promising results.
Fil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Strickert, Marc. Leibniz Institute of Plant Genetics and Crop Plant Research; Alemania
Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Materia
Adaptive matrix metrics
QSAR
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:11336/62306
Acceder
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spelling Adaptive matrix metrics for molecular descriptor assessment in QSPR classificationSoto, Axel JuanStrickert, MarcVazquez, Gustavo EstebanAdaptive matrix metricsQSARhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1QSPR methods represent a useful approach in the drug discovery process, since they allow to predict in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions. In this work, a matrix-based method is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy. A recently proposed method making use of this concept is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property shows promising results.Fil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaFil: Strickert, Marc. Leibniz Institute of Plant Genetics and Crop Plant Research; AlemaniaFil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; ArgentinaChemistry Central2010-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/62306Soto, Axel Juan; Strickert, Marc; Vazquez, Gustavo Esteban; Adaptive matrix metrics for molecular descriptor assessment in QSPR classification; Chemistry Central; Journal of Cheminformatics; 2; s1; 3-2010; 47-471758-2946CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1186/1758-2946-2-S1-P47info:eu-repo/semantics/altIdentifier/url/https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-2-S1-P47info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:55:03Zoai:ri.conicet.gov.ar:11336/62306instacron: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 09:55:04.072CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
title Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
spellingShingle Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
Soto, Axel Juan
Adaptive matrix metrics
QSAR
title_short Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
title_full Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
title_fullStr Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
title_full_unstemmed Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
title_sort Adaptive matrix metrics for molecular descriptor assessment in QSPR classification
dc.creator.none.fl_str_mv Soto, Axel Juan
Strickert, Marc
Vazquez, Gustavo Esteban
author Soto, Axel Juan
author_facet Soto, Axel Juan
Strickert, Marc
Vazquez, Gustavo Esteban
author_role author
author2 Strickert, Marc
Vazquez, Gustavo Esteban
author2_role author
author
dc.subject.none.fl_str_mv Adaptive matrix metrics
QSAR
topic Adaptive matrix metrics
QSAR
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv QSPR methods represent a useful approach in the drug discovery process, since they allow to predict in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions. In this work, a matrix-based method is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy. A recently proposed method making use of this concept is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property shows promising results.
Fil: Soto, Axel Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
Fil: Strickert, Marc. Leibniz Institute of Plant Genetics and Crop Plant Research; Alemania
Fil: Vazquez, Gustavo Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina
description QSPR methods represent a useful approach in the drug discovery process, since they allow to predict in advance biological or physicochemical properties of a candidate drug. For this goal, it is necessary that the QSPR method be as accurate as possible to provide reliable predictions. Moreover, the selection of the molecular descriptors is an important task to create QSPR prediction models of low complexity which, at the same time, provide accurate predictions. In this work, a matrix-based method is used to transform the original data space of chemical compounds into an alternative space where compounds with different target properties can be better separated. For using this approach, QSPR is considered as a classification problem. The advantage of using adaptive matrix metrics is twofold: it can be used to identify important molecular descriptors and at the same time it allows improving the classification accuracy. A recently proposed method making use of this concept is extended to multi-class data. The new method is related to linear discriminant analysis and shows better results at yet higher computational costs. An application for relating chemical descriptors to hydrophobicity property shows promising results.
publishDate 2010
dc.date.none.fl_str_mv 2010-03
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/11336/62306
Soto, Axel Juan; Strickert, Marc; Vazquez, Gustavo Esteban; Adaptive matrix metrics for molecular descriptor assessment in QSPR classification; Chemistry Central; Journal of Cheminformatics; 2; s1; 3-2010; 47-47
1758-2946
CONICET Digital
CONICET
url http://hdl.handle.net/11336/62306
identifier_str_mv Soto, Axel Juan; Strickert, Marc; Vazquez, Gustavo Esteban; Adaptive matrix metrics for molecular descriptor assessment in QSPR classification; Chemistry Central; Journal of Cheminformatics; 2; s1; 3-2010; 47-47
1758-2946
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1186/1758-2946-2-S1-P47
info:eu-repo/semantics/altIdentifier/url/https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-2-S1-P47
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 Chemistry Central
publisher.none.fl_str_mv Chemistry Central
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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