Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides

Autores
Goodarzi, Mohammad; Ortiz, Erlinda del Valle; Coelho, Leandro dos S.; Duchowicz, Pablo Román
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative StructureeProperty Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanolewater partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the LevenbergeMarquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.
Fil: Goodarzi, Mohammad. 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: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Coelho, Leandro dos S.. Universidade Estadual Do Parana (unespar);
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
QSPR-QSAR Theory
Replacement method
Artificial neural networks
Henrys law constant
Dragon molecular descriptors
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:11336/281311
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/281311
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticidesGoodarzi, MohammadOrtiz, Erlinda del ValleCoelho, Leandro dos S.Duchowicz, Pablo RománQSPR-QSAR TheoryReplacement methodArtificial neural networksHenrys law constantDragon molecular descriptorshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative StructureeProperty Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanolewater partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the LevenbergeMarquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.Fil: Goodarzi, Mohammad. 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: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Coelho, Leandro dos S.. Universidade Estadual Do Parana (unespar);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; ArgentinaPergamon-Elsevier Science Ltd2010-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/281311Goodarzi, Mohammad; Ortiz, Erlinda del Valle; Coelho, Leandro dos S.; Duchowicz, Pablo Román; Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides; Pergamon-Elsevier Science Ltd; Atmospheric Environment; 44; 26; 5-2010; 3179-31861352-2310CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1352231010003985info:eu-repo/semantics/altIdentifier/doi/10.1016/j.atmosenv.2010.05.025info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-02-11T12:05:11Zoai:ri.conicet.gov.ar:11336/281311instacron: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:34982026-02-11 12:05:11.327CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
title Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
spellingShingle Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
Goodarzi, Mohammad
QSPR-QSAR Theory
Replacement method
Artificial neural networks
Henrys law constant
Dragon molecular descriptors
title_short Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
title_full Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
title_fullStr Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
title_full_unstemmed Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
title_sort Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides
dc.creator.none.fl_str_mv Goodarzi, Mohammad
Ortiz, Erlinda del Valle
Coelho, Leandro dos S.
Duchowicz, Pablo Román
author Goodarzi, Mohammad
author_facet Goodarzi, Mohammad
Ortiz, Erlinda del Valle
Coelho, Leandro dos S.
Duchowicz, Pablo Román
author_role author
author2 Ortiz, Erlinda del Valle
Coelho, Leandro dos S.
Duchowicz, Pablo Román
author2_role author
author
author
dc.subject.none.fl_str_mv QSPR-QSAR Theory
Replacement method
Artificial neural networks
Henrys law constant
Dragon molecular descriptors
topic QSPR-QSAR Theory
Replacement method
Artificial neural networks
Henrys law constant
Dragon 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 This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative StructureeProperty Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanolewater partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the LevenbergeMarquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.
Fil: Goodarzi, Mohammad. 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: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Coelho, Leandro dos S.. Universidade Estadual Do Parana (unespar);
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 This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative StructureeProperty Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanolewater partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the LevenbergeMarquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.
publishDate 2010
dc.date.none.fl_str_mv 2010-05
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/11336/281311
Goodarzi, Mohammad; Ortiz, Erlinda del Valle; Coelho, Leandro dos S.; Duchowicz, Pablo Román; Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides; Pergamon-Elsevier Science Ltd; Atmospheric Environment; 44; 26; 5-2010; 3179-3186
1352-2310
CONICET Digital
CONICET
url http://hdl.handle.net/11336/281311
identifier_str_mv Goodarzi, Mohammad; Ortiz, Erlinda del Valle; Coelho, Leandro dos S.; Duchowicz, Pablo Román; Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides; Pergamon-Elsevier Science Ltd; Atmospheric Environment; 44; 26; 5-2010; 3179-3186
1352-2310
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S1352231010003985
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.atmosenv.2010.05.025
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
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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_ 1856945305220546560
score 12.930639

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