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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/43785
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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 Técnicas2025-09-29T10:19:32Zoai: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:34982025-09-29 10:19:32.386CONICET 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 |
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1844614167480238080 |
score |
13.070432 |