Three-class classification models of logS and logP derived by using GA-CG-SVM approach.
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.
Zhang
Hui
H
State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, People's Republic of China.
Xiang
Ming-Li
ML
Ma
Chang-Ying
CY
Huang
Qi
Q
Li
Wei
W
Xie
Yang
Y
Wei
Yu-Quan
YQ
Yang
Sheng-Yong
SY
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*
D014867:Water