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dc.contributor.author
Duchowicz, Pablo Román
dc.contributor.author
Bennardi, Daniel Oscar
dc.contributor.author
Ortiz, Erlinda del Valle
dc.contributor.author
Comelli, Nieves Carolina
dc.date.available
2022-09-27T12:56:23Z
dc.date.issued
2020-12
dc.identifier.citation
Duchowicz, Pablo Román; Bennardi, Daniel Oscar; Ortiz, Erlinda del Valle; Comelli, Nieves Carolina; QSAR models for the fumigant activity prediction of essential oils; Elsevier Science Inc.; Journal Of Molecular Graphics & Modelling; 101; 12-2020; 1-7
dc.identifier.issn
1093-3263
dc.identifier.uri
http://hdl.handle.net/11336/170589
dc.description.abstract
The Quantitative Structure-Activity Relationships (QSAR) theory, which allows predicting the insecticidal activity of chemical compounds through calculations from the molecular structure, is applied on 23 essential oils composed of 402 structurally diverse compounds at different chemical compositions. A large number of 114,871 conformation-independent molecular descriptors are computed through different types of freely available open-source programs. Mixture descriptors are calculated based on molecular descriptors of the essential oil components and their composition. The best resulting three-descriptor linear regression models are established through the Replacement Method variable subset selection approach. The results obtained in the present work are interesting for predicting the fumigant activity of these essential oil complex mixtures, by means of simple non-conformational QSAR models.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science Inc.
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ESSENTIAL OILS
dc.subject
FUMIGANT ACTIVITY
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MOLECULAR DESCRIPTORS
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QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS
dc.subject.classification
Otras Ciencias Químicas
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
QSAR models for the fumigant activity prediction of essential oils
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-26T17:49:37Z
dc.identifier.eissn
1873-4243
dc.journal.volume
101
dc.journal.pagination
1-7
dc.journal.pais
Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Bennardi, Daniel Oscar. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Departamento de Ciencias Exactas. Cátedra de Química Orgánica; Argentina
dc.description.fil
Fil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de Tecnología y Ciencias Aplicadas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia de Catamarca. Universidad Nacional de Catamarca. Centro de Investigaciones y Transferencia de Catamarca; Argentina
dc.journal.title
Journal Of Molecular Graphics & Modelling
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1093326320305404?via%3Dihub
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.jmgm.2020.107751
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