pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorGoodarzi, Mohammad-
Autor(es): dc.creatorFreitas, Matheus P.-
Autor(es): dc.creatorWu, Chih H.-
Autor(es): dc.creatorDuchowicz, Pablo R.-
Data de aceite: dc.date.accessioned2026-02-09T11:39:45Z-
Data de disponibilização: dc.date.available2026-02-09T11:39:45Z-
Data de envio: dc.date.issued2020-07-12-
Data de envio: dc.date.issued2020-07-12-
Data de envio: dc.date.issued2010-04-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/41805-
Fonte completa do material: dc.identifierhttps://www.sciencedirect.com/science/article/abs/pii/S0169743910000274-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1144637-
Descrição: dc.descriptionThe pKa values of a series of 107 indicators have been modeled by means of a quantitative structure–property relationship (QSPR) approach based on physicochemical descriptors and different variable selection and regression methods. A genetic algorithm/least square support vector regression (GA-LSSVR) model gave the most accurate estimations/predictions, with squared correlation coefficients of 0.90 and 0.89 for the training and test set compounds, respectively. The prediction ability of this model was found to be superior to that based on support vector machine regression alone, revealing the important effect of selecting suitable descriptors during a QSPR modeling. Moreover, the GA-LSSVR model showed higher predictive capability than linear methods, demonstrating the influence of nonlinearity on the modeling of pKa values, an extremely useful parameter in the analytical sciences.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceChemometrics and Intelligent Laboratory Systems-
Palavras-chave: dc.subjectpKa-
Palavras-chave: dc.subjectpH indicators-
Palavras-chave: dc.subjectQuantitative structure-property relationship-
Palavras-chave: dc.subjectSupport vector machines-
Palavras-chave: dc.subjectGA-LSSVR-
Palavras-chave: dc.subjectGenetic algorithm-least square support vector regression (GA-LSSVR)-
Título: dc.titlepKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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