QSAR study for carcinogenicity in a large set of organic compounds

Autores
Duchowicz, Pablo Román; Comelli, Nieves Carolina; Ortiz, Erlinda del Valle; Castro, Eduardo Alberto
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.
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: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; Argentina
Fil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias 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
ADMET
CARCINOGENICITY
MOLECULAR DESCRIPTORS
MULTIVARIABLE LINEAR REGRESSION ANALYSIS
QSAR THEORY
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:11336/81029
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling QSAR study for carcinogenicity in a large set of organic compoundsDuchowicz, Pablo RománComelli, Nieves CarolinaOrtiz, Erlinda del ValleCastro, Eduardo AlbertoADMETCARCINOGENICITYMOLECULAR DESCRIPTORSMULTIVARIABLE LINEAR REGRESSION ANALYSISQSAR THEORYhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.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; ArgentinaFil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias 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; ArgentinaBentham Science Publishers2012-11info: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/11336/81029Duchowicz, Pablo Román; Comelli, Nieves Carolina; Ortiz, Erlinda del Valle; Castro, Eduardo Alberto; QSAR study for carcinogenicity in a large set of organic compounds; Bentham Science Publishers; Current Drug Safety; 7; 4; 11-2012; 282-2881574-8863CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.currentdrugsafety.com/articles/104955/qsar-study-for-carcinogenicity-in-a-large-set-of-organic-compoundsinfo:eu-repo/semantics/altIdentifier/doi/10.2174/157488612804096623info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:09:26Zoai:ri.conicet.gov.ar:11336/81029instacron: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 10:09:26.65CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv QSAR study for carcinogenicity in a large set of organic compounds
title QSAR study for carcinogenicity in a large set of organic compounds
spellingShingle QSAR study for carcinogenicity in a large set of organic compounds
Duchowicz, Pablo Román
ADMET
CARCINOGENICITY
MOLECULAR DESCRIPTORS
MULTIVARIABLE LINEAR REGRESSION ANALYSIS
QSAR THEORY
title_short QSAR study for carcinogenicity in a large set of organic compounds
title_full QSAR study for carcinogenicity in a large set of organic compounds
title_fullStr QSAR study for carcinogenicity in a large set of organic compounds
title_full_unstemmed QSAR study for carcinogenicity in a large set of organic compounds
title_sort QSAR study for carcinogenicity in a large set of organic compounds
dc.creator.none.fl_str_mv Duchowicz, Pablo Román
Comelli, Nieves Carolina
Ortiz, Erlinda del Valle
Castro, Eduardo Alberto
author Duchowicz, Pablo Román
author_facet Duchowicz, Pablo Román
Comelli, Nieves Carolina
Ortiz, Erlinda del Valle
Castro, Eduardo Alberto
author_role author
author2 Comelli, Nieves Carolina
Ortiz, Erlinda del Valle
Castro, Eduardo Alberto
author2_role author
author
author
dc.subject.none.fl_str_mv ADMET
CARCINOGENICITY
MOLECULAR DESCRIPTORS
MULTIVARIABLE LINEAR REGRESSION ANALYSIS
QSAR THEORY
topic ADMET
CARCINOGENICITY
MOLECULAR DESCRIPTORS
MULTIVARIABLE LINEAR REGRESSION ANALYSIS
QSAR THEORY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.
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: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; Argentina
Fil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias 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 In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.
publishDate 2012
dc.date.none.fl_str_mv 2012-11
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/11336/81029
Duchowicz, Pablo Román; Comelli, Nieves Carolina; Ortiz, Erlinda del Valle; Castro, Eduardo Alberto; QSAR study for carcinogenicity in a large set of organic compounds; Bentham Science Publishers; Current Drug Safety; 7; 4; 11-2012; 282-288
1574-8863
CONICET Digital
CONICET
url http://hdl.handle.net/11336/81029
identifier_str_mv Duchowicz, Pablo Román; Comelli, Nieves Carolina; Ortiz, Erlinda del Valle; Castro, Eduardo Alberto; QSAR study for carcinogenicity in a large set of organic compounds; Bentham Science Publishers; Current Drug Safety; 7; 4; 11-2012; 282-288
1574-8863
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.currentdrugsafety.com/articles/104955/qsar-study-for-carcinogenicity-in-a-large-set-of-organic-compounds
info:eu-repo/semantics/altIdentifier/doi/10.2174/157488612804096623
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 Bentham Science Publishers
publisher.none.fl_str_mv Bentham Science Publishers
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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