title: Three-class classification models of logS and logP derived by using GA-CG-SVM approach.
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.
authors.affiliation: State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Ming-Li
authors.initials: ML
authors.lastname: Xiang
authors.forename: Chang-Ying
authors.initials: CY
authors.lastname: Ma
authors.forename: Qi
authors.initials: Q
authors.lastname: Huang
authors.forename: Wei
authors.initials: W
authors.lastname: Li
authors.forename: Yang
authors.initials: Y
authors.lastname: Xie
authors.forename: Yu-Quan
authors.initials: YQ
authors.lastname: Wei
authors.forename: Sheng-Yong
authors.initials: SY
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005823:Genetics; D057927:Hydrophobic and Hydrophilic Interactions; D008956:Models, Chemical; D012680:Sensitivity and Specificity; D012995:Solubility; D014867:Water / Q000737:chemistry*
chemical_list: D014867:Water