Journal
MOLECULAR DIVERSITY
Volume 13, Issue 2, Pages 261-268Publisher
SPRINGER
DOI: 10.1007/s11030-009-9108-1
Keywords
Aqueous solubility; Lipophilicity; Feature selection; Parameter optimization; Support vector machine (SVM)
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Funding
- 863 Hi-Tech Program [2006AA020402]
- National Natural Science Foundation of China [30772651, 20872100]
- Youth Foundation of Sichuan Province [08ZQ026-030]
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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.
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