Three-class classification models of logS and logP derived by using GA-CG-SVM approach.
Mol Divers
; 13(2): 261-8, 2009 May.
Article
em En
| MEDLINE
| ID: mdl-19184630
ABSTRACT
In this investigation, three-class classification models of aqueous solubility (logS) and lipophilicity (logP) have been developed by using a support vector machine (SVM) method combined with a genetic algorithm (GA) for feature selection and a conjugate gradient method (CG) for parameter optimization. A 5-fold cross-validation and an independent test set method were used to evaluate the SVM classification models. For logS, the overall prediction accuracy is 87.1% for training set and 90.0% for test set. For logP, the overall prediction accuracy is 81.0% for training set and 82.0% for test set. In general, for both logS and logP, the prediction accuracies of three-class models are slightly lower by several percent than those of two-class models. A comparison between the performance of GA-CG-SVM models and that of GA-SVM models shows that the SVM parameter optimization has a significant impact on the quality of SVM classification model.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Inteligência Artificial
/
Água
/
Modelos Químicos
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Mol Divers
Assunto da revista:
BIOLOGIA MOLECULAR
Ano de publicação:
2009
Tipo de documento:
Article