NDLI: Three-class classification models of logS and logP derived by using GA–CG

Content Provider	Springer Nature Link
Author	Zhang, Hui Xiang, Ming Li Ma, Chang Ying Huang, Qi Li, Wei Xie, Yang Wei, Yu Quan Yang, Sheng Yong
Copyright Year	2009
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.
Starting Page	261
Ending Page	268
Page Count	8
File Format	PDF
ISSN	13811991
Journal	Molecular Diversity
Volume Number	13
Issue Number	2
e-ISSN	1573501X
Language	English
Publisher	Springer Netherlands
Publisher Date	2009-01-31
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Aqueous solubility Lipophilicity Feature selection Parameter optimization Support vector machine (SVM) Biochemistry Organic Chemistry Polymer Sciences Pharmacy
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Medicine Drug Discovery Molecular Biology Physical and Theoretical Chemistry Information Systems Catalysis Inorganic Chemistry

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