Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides

Autores
Goodarzi, Mohammad; Coelho, Leandro dos Santos; Honarparvar, Bahareh; Ortiz, Erlinda del Valle; Duchowicz, Pablo Román
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.
Fil: Goodarzi, Mohammad. Katholikie Universiteit Leuven; Bélgica
Fil: Coelho, Leandro dos Santos. Universidade Federal do Paraná; Brasil
Fil: Honarparvar, Bahareh. University of KwaZulu-Natal; Sudáfrica
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: 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
Teoría Qspr
Pesticides
Vapor Pressure
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/43785
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticidesGoodarzi, MohammadCoelho, Leandro dos SantosHonarparvar, BaharehOrtiz, Erlinda del ValleDuchowicz, Pablo RománTeoría QsprPesticidesVapor Pressurehttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.Fil: Goodarzi, Mohammad. Katholikie Universiteit Leuven; BélgicaFil: Coelho, Leandro dos Santos. Universidade Federal do Paraná; BrasilFil: Honarparvar, Bahareh. University of KwaZulu-Natal; SudáfricaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: 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; ArgentinaAcademic Press Inc Elsevier Science2016-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/43785Goodarzi, Mohammad; Coelho, Leandro dos Santos; Honarparvar, Bahareh; Ortiz, Erlinda del Valle; Duchowicz, Pablo Román; Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides; Academic Press Inc Elsevier Science; Ecotoxicology and Environmental Safety; 128; 6-2016; 52-600147-6513CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoenv.2016.01.020info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0147651316300203info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:16:41Zoai:ri.conicet.gov.ar:11336/43785instacron: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:34982025-10-15 15:16:42.153CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
title Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
spellingShingle Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
Goodarzi, Mohammad
Teoría Qspr
Pesticides
Vapor Pressure
title_short Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
title_full Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
title_fullStr Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
title_full_unstemmed Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
title_sort Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides
dc.creator.none.fl_str_mv Goodarzi, Mohammad
Coelho, Leandro dos Santos
Honarparvar, Bahareh
Ortiz, Erlinda del Valle
Duchowicz, Pablo Román
author Goodarzi, Mohammad
author_facet Goodarzi, Mohammad
Coelho, Leandro dos Santos
Honarparvar, Bahareh
Ortiz, Erlinda del Valle
Duchowicz, Pablo Román
author_role author
author2 Coelho, Leandro dos Santos
Honarparvar, Bahareh
Ortiz, Erlinda del Valle
Duchowicz, Pablo Román
author2_role author
author
author
author
dc.subject.none.fl_str_mv Teoría Qspr
Pesticides
Vapor Pressure
topic Teoría Qspr
Pesticides
Vapor Pressure
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.
Fil: Goodarzi, Mohammad. Katholikie Universiteit Leuven; Bélgica
Fil: Coelho, Leandro dos Santos. Universidade Federal do Paraná; Brasil
Fil: Honarparvar, Bahareh. University of KwaZulu-Natal; Sudáfrica
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: 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 The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.
publishDate 2016
dc.date.none.fl_str_mv 2016-06
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/11336/43785
Goodarzi, Mohammad; Coelho, Leandro dos Santos; Honarparvar, Bahareh; Ortiz, Erlinda del Valle; Duchowicz, Pablo Román; Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides; Academic Press Inc Elsevier Science; Ecotoxicology and Environmental Safety; 128; 6-2016; 52-60
0147-6513
CONICET Digital
CONICET
url http://hdl.handle.net/11336/43785
identifier_str_mv Goodarzi, Mohammad; Coelho, Leandro dos Santos; Honarparvar, Bahareh; Ortiz, Erlinda del Valle; Duchowicz, Pablo Román; Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides; Academic Press Inc Elsevier Science; Ecotoxicology and Environmental Safety; 128; 6-2016; 52-60
0147-6513
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoenv.2016.01.020
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0147651316300203
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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_ 1846083315575029760
score 13.22299

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