Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors

Autores
Duchowicz, Pablo Román
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiledfrom the ChEMBL database and studied by means of a conformation-independent quantitativestructure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptorsare explored with the main intention of capturing the most relevant structural characteristics affectingthe bioactivity. The structural descriptors are derived with different freeware, such as PaDEL,Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSARresults. The best multivariable linear regression models are found with the replacement methodvariable subset selection technique. The balanced subsets method partitions the dataset into training,validation, and test sets. It is found that the proposed linear QSAR model improves previouslyreported models by leading to a simpler alternative structure-activity relationship.
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
polo-like kinase-1 inhibitors
QSAR
half-maximal inhibitory concentration
replacement method
molecular descriptors
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/102533
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/102533
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Linear Regression QSAR Models for Polo-Like Kinase-1 InhibitorsDuchowicz, Pablo Románpolo-like kinase-1 inhibitorsQSARhalf-maximal inhibitory concentrationreplacement methodmolecular descriptorshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiledfrom the ChEMBL database and studied by means of a conformation-independent quantitativestructure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptorsare explored with the main intention of capturing the most relevant structural characteristics affectingthe bioactivity. The structural descriptors are derived with different freeware, such as PaDEL,Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSARresults. The best multivariable linear regression models are found with the replacement methodvariable subset selection technique. The balanced subsets method partitions the dataset into training,validation, and test sets. It is found that the proposed linear QSAR model improves previouslyreported models by leading to a simpler alternative structure-activity relationship.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; ArgentinaMDPI2018-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/102533Duchowicz, Pablo Román; Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors; MDPI; Cells; 7; 2; 2-2018; 1-112073-4409CONICET DigitalCONICETengVer también http://hdl.handle.net/11336/5267info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850101/info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4409/7/2/13info:eu-repo/semantics/altIdentifier/doi/10.3390/cells7020013info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:43:33Zoai:ri.conicet.gov.ar:11336/102533instacron: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 09:43:33.544CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
spellingShingle Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
Duchowicz, Pablo Román
polo-like kinase-1 inhibitors
QSAR
half-maximal inhibitory concentration
replacement method
molecular descriptors
title_short Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_full Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_fullStr Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_full_unstemmed Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
title_sort Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors
dc.creator.none.fl_str_mv Duchowicz, Pablo Román
author Duchowicz, Pablo Román
author_facet Duchowicz, Pablo Román
author_role author
dc.subject.none.fl_str_mv polo-like kinase-1 inhibitors
QSAR
half-maximal inhibitory concentration
replacement method
molecular descriptors
topic polo-like kinase-1 inhibitors
QSAR
half-maximal inhibitory concentration
replacement method
molecular descriptors
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiledfrom the ChEMBL database and studied by means of a conformation-independent quantitativestructure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptorsare explored with the main intention of capturing the most relevant structural characteristics affectingthe bioactivity. The structural descriptors are derived with different freeware, such as PaDEL,Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSARresults. The best multivariable linear regression models are found with the replacement methodvariable subset selection technique. The balanced subsets method partitions the dataset into training,validation, and test sets. It is found that the proposed linear QSAR model improves previouslyreported models by leading to a simpler alternative structure-activity relationship.
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 A structurally diverse dataset of 530 polo-like kinase-1 (PLK1) inhibitors is compiledfrom the ChEMBL database and studied by means of a conformation-independent quantitativestructure-activity relationship (QSAR) approach. A large number (26,761) of molecular descriptorsare explored with the main intention of capturing the most relevant structural characteristics affectingthe bioactivity. The structural descriptors are derived with different freeware, such as PaDEL,Mold2, and QuBiLs-MAS; such descriptor software complements each other and improves the QSARresults. The best multivariable linear regression models are found with the replacement methodvariable subset selection technique. The balanced subsets method partitions the dataset into training,validation, and test sets. It is found that the proposed linear QSAR model improves previouslyreported models by leading to a simpler alternative structure-activity relationship.
publishDate 2018
dc.date.none.fl_str_mv 2018-02
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/11336/102533
Duchowicz, Pablo Román; Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors; MDPI; Cells; 7; 2; 2-2018; 1-11
2073-4409
CONICET Digital
CONICET
url http://hdl.handle.net/11336/102533
identifier_str_mv Duchowicz, Pablo Román; Linear Regression QSAR Models for Polo-Like Kinase-1 Inhibitors; MDPI; Cells; 7; 2; 2-2018; 1-11
2073-4409
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Ver también http://hdl.handle.net/11336/5267
info:eu-repo/semantics/altIdentifier/url/https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5850101/
info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2073-4409/7/2/13
info:eu-repo/semantics/altIdentifier/doi/10.3390/cells7020013
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
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
_version_ 1842268609354661888
score 13.13397

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