Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/41805
metadata.artigo.dc.title: pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression
metadata.artigo.dc.creator: Goodarzi, Mohammad
Freitas, Matheus P.
Wu, Chih H.
Duchowicz, Pablo R.
metadata.artigo.dc.subject: pKa
pH indicators
Quantitative structure-property relationship
Support vector machines
GA-LSSVR
Genetic algorithm-least square support vector regression (GA-LSSVR)
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Apr-2010
metadata.artigo.dc.identifier.citation: GOODARZI, M. et al. pKa modeling and prediction of a series of pH indicators through genetic algorithm-least square support vector regression. Chemometrics and Intelligent Laboratory Systems, [S.l.], v. 101, n. 2, p. 102-109, Apr. 2010. DOI: 10.1016/j.chemolab.2010.02.003.
metadata.artigo.dc.description.abstract: The 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.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/abs/pii/S0169743910000274
http://repositorio.ufla.br/jspui/handle/1/41805
metadata.artigo.dc.language: en_US
Appears in Collections:DQI - Artigos publicados em periódicos

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