QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1

Autores
Comelli, Nieves Carolina; Duchowicz, Pablo Román; Castro, Eduardo Alberto
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (log IC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure Doptimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (R2 test). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.
Fil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; Argentina
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Materia
Chemoinformatics
Multivariate Linear Regression Analysis
Polo-Like Kinase 1 (Plk1) Inhibitors
Thiophene And Imidazopyridines Derivatives
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:11336/5267
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/5267
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1Comelli, Nieves CarolinaDuchowicz, Pablo RománCastro, Eduardo AlbertoChemoinformaticsMultivariate Linear Regression AnalysisPolo-Like Kinase 1 (Plk1) InhibitorsThiophene And Imidazopyridines Derivativeshttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (log IC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure Doptimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (R2 test). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.Fil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; ArgentinaElsevier Science2014-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/5267Comelli, Nieves Carolina; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1; Elsevier Science; European Journal Of Pharmaceutical Sciences; 62; 5-2014; 171-1790928-0987enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0928098714002589info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ejps.2014.05.029info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:53:40Zoai:ri.conicet.gov.ar:11336/5267instacron: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 09:53:40.394CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
title QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
spellingShingle QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
Comelli, Nieves Carolina
Chemoinformatics
Multivariate Linear Regression Analysis
Polo-Like Kinase 1 (Plk1) Inhibitors
Thiophene And Imidazopyridines Derivatives
title_short QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
title_full QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
title_fullStr QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
title_full_unstemmed QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
title_sort QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1
dc.creator.none.fl_str_mv Comelli, Nieves Carolina
Duchowicz, Pablo Román
Castro, Eduardo Alberto
author Comelli, Nieves Carolina
author_facet Comelli, Nieves Carolina
Duchowicz, Pablo Román
Castro, Eduardo Alberto
author_role author
author2 Duchowicz, Pablo Román
Castro, Eduardo Alberto
author2_role author
author
dc.subject.none.fl_str_mv Chemoinformatics
Multivariate Linear Regression Analysis
Polo-Like Kinase 1 (Plk1) Inhibitors
Thiophene And Imidazopyridines Derivatives
topic Chemoinformatics
Multivariate Linear Regression Analysis
Polo-Like Kinase 1 (Plk1) Inhibitors
Thiophene And Imidazopyridines Derivatives
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (log IC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure Doptimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (R2 test). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.
Fil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; Argentina
Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
Fil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas; Argentina
description The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (log IC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure Doptimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (R2 test). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method.
publishDate 2014
dc.date.none.fl_str_mv 2014-05
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/11336/5267
Comelli, Nieves Carolina; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1; Elsevier Science; European Journal Of Pharmaceutical Sciences; 62; 5-2014; 171-179
0928-0987
url http://hdl.handle.net/11336/5267
identifier_str_mv Comelli, Nieves Carolina; Duchowicz, Pablo Román; Castro, Eduardo Alberto; QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1; Elsevier Science; European Journal Of Pharmaceutical Sciences; 62; 5-2014; 171-179
0928-0987
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0928098714002589
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ejps.2014.05.029
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
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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