Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds

Autores
Mercader, Andrew Gustavo; Goodarzi, Mohammad; Duchowicz, Pablo Román; Fernández, Francisco Marcelo; Castro, Eduardo Alberto
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pKa) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules. © 2010 John Wiley & Sons A/S.
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. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina
Fil: Goodarzi, Mohammad. Islamic Azad University; Iraq
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
Fil: Fernández, Francisco Marcelo. 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: Castro, Eduardo Alberto. 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
ENHANCED REPLACEMENT METHOD
PHARMACEUTICAL COMPOUNDS
PKA
QSPR
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:11336/67781
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/67781
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predictive QSPR study of the dissociation constants of diverse pharmaceutical compoundsMercader, Andrew GustavoGoodarzi, MohammadDuchowicz, Pablo RománFernández, Francisco MarceloCastro, Eduardo AlbertoENHANCED REPLACEMENT METHODPHARMACEUTICAL COMPOUNDSPKAQSPRhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pKa) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules. © 2010 John Wiley & Sons A/S.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. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Goodarzi, Mohammad. Islamic Azad University; IraqFil: 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; ArgentinaFil: Fernández, Francisco Marcelo. 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: Castro, Eduardo Alberto. 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; ArgentinaWiley Blackwell Publishing, Inc2010-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/67781Mercader, Andrew Gustavo; Goodarzi, Mohammad; Duchowicz, Pablo Román; Fernández, Francisco Marcelo; Castro, Eduardo Alberto; Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds; Wiley Blackwell Publishing, Inc; Chemical Biology & Drug Design; 76; 5; 11-2010; 433-4401747-0277CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1111/j.1747-0285.2010.01033.xinfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1111/j.1747-0285.2010.01033.xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:42:29Zoai:ri.conicet.gov.ar:11336/67781instacron: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 09:42:29.527CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
title Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
spellingShingle Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
Mercader, Andrew Gustavo
ENHANCED REPLACEMENT METHOD
PHARMACEUTICAL COMPOUNDS
PKA
QSPR
title_short Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
title_full Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
title_fullStr Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
title_full_unstemmed Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
title_sort Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds
dc.creator.none.fl_str_mv Mercader, Andrew Gustavo
Goodarzi, Mohammad
Duchowicz, Pablo Román
Fernández, Francisco Marcelo
Castro, Eduardo Alberto
author Mercader, Andrew Gustavo
author_facet Mercader, Andrew Gustavo
Goodarzi, Mohammad
Duchowicz, Pablo Román
Fernández, Francisco Marcelo
Castro, Eduardo Alberto
author_role author
author2 Goodarzi, Mohammad
Duchowicz, Pablo Román
Fernández, Francisco Marcelo
Castro, Eduardo Alberto
author2_role author
author
author
author
dc.subject.none.fl_str_mv ENHANCED REPLACEMENT METHOD
PHARMACEUTICAL COMPOUNDS
PKA
QSPR
topic ENHANCED REPLACEMENT METHOD
PHARMACEUTICAL COMPOUNDS
PKA
QSPR
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 objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pKa) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules. © 2010 John Wiley & Sons A/S.
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. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina
Fil: Goodarzi, Mohammad. Islamic Azad University; Iraq
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
Fil: Fernández, Francisco Marcelo. 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: Castro, Eduardo Alberto. 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 objective of the article was to perform a predictive analysis, based on quantitative structure-property relationships, of the dissociation constants (pKa) of different medicinal compounds (e.g., salicylic acid, salbutamol, lidocaine). Given the importance of this property in medicinal chemistry, it is of interest to develop theoretical methods for its prediction. The descriptors selection from a pool containing more than a thousand geometrical, topological, quantum-mechanical, and electronic types of descriptors was performed using the enhanced replacement method. Genetic algorithm and the replacement method (RM) techniques were used as reference points. A new methodology for the selection of the optimal number of descriptors to include in a model was presented and successfully used, showing that the best model should contain four descriptors. The best quantitative structure-property relationships linear model constructed using 62 molecular structures not previously used in this type of quantitative structure-property study showed good predictive attributes. The root mean squared error of the 26 molecules test set was 0.5600. The analysis of the quantitative structure-property relationships model suggests that the dissociation constants depend significantly on the number of acceptor atoms for H-bonds and on the number of carboxylic acids present in the molecules. © 2010 John Wiley & Sons A/S.
publishDate 2010
dc.date.none.fl_str_mv 2010-11
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/11336/67781
Mercader, Andrew Gustavo; Goodarzi, Mohammad; Duchowicz, Pablo Román; Fernández, Francisco Marcelo; Castro, Eduardo Alberto; Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds; Wiley Blackwell Publishing, Inc; Chemical Biology & Drug Design; 76; 5; 11-2010; 433-440
1747-0277
CONICET Digital
CONICET
url http://hdl.handle.net/11336/67781
identifier_str_mv Mercader, Andrew Gustavo; Goodarzi, Mohammad; Duchowicz, Pablo Román; Fernández, Francisco Marcelo; Castro, Eduardo Alberto; Predictive QSPR study of the dissociation constants of diverse pharmaceutical compounds; Wiley Blackwell Publishing, Inc; Chemical Biology & Drug Design; 76; 5; 11-2010; 433-440
1747-0277
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1747-0285.2010.01033.x
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1111/j.1747-0285.2010.01033.x
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
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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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