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.
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
Materia
Química
Ciencias Exactas
Admet
Carcinogenicity
Molecular descriptors
Multivariable linear regression analysis
QSAR theory
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/97231

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oai_identifier_str oai:sedici.unlp.edu.ar:10915/97231
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling QSAR study for carcinogenicity in a large set of organic compoundsDuchowicz, Pablo RománComelli, Nieves CarolinaOrtiz, Erlinda del ValleCastro, Eduardo AlbertoQuímicaCiencias ExactasAdmetCarcinogenicityMolecular descriptorsMultivariable linear regression analysisQSAR theoryIn 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.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf282-288http://sedici.unlp.edu.ar/handle/10915/97231enginfo:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/81029info: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/issn/1574-8863info:eu-repo/semantics/altIdentifier/doi/10.2174/157488612804096623info:eu-repo/semantics/altIdentifier/hdl/11336/81029info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-11-05T12:59:47Zoai:sedici.unlp.edu.ar:10915/97231Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-05 12:59:47.532SEDICI (UNLP) - Universidad Nacional de La Platafalse
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
Química
Ciencias Exactas
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 Química
Ciencias Exactas
Admet
Carcinogenicity
Molecular descriptors
Multivariable linear regression analysis
QSAR theory
topic Química
Ciencias Exactas
Admet
Carcinogenicity
Molecular descriptors
Multivariable linear regression analysis
QSAR theory
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.
Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas
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
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
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://sedici.unlp.edu.ar/handle/10915/97231
url http://sedici.unlp.edu.ar/handle/10915/97231
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://ri.conicet.gov.ar/11336/81029
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/issn/1574-8863
info:eu-repo/semantics/altIdentifier/doi/10.2174/157488612804096623
info:eu-repo/semantics/altIdentifier/hdl/11336/81029
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
282-288
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
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instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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