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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/97231
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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 |
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2012 |
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eng |
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