Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines.
The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors.
Li
Jiazhong
J
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
Liu
Huanxiang
H
Yao
Xiaojun
X
Liu
Mancang
M
Hu
Zhide
Z
Fan
Botao
B
D018844:Cyclin-Dependent Kinases / Q000037:antagonists & inhibitors*; D007211:Indoles / Q000737:chemistry*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D000078183:Oxindoles; D047428:Protein Kinase Inhibitors / Q000494:pharmacology*; D013329:Structure-Activity Relationship
D007211:Indoles; D000078183:Oxindoles; D047428:Protein Kinase Inhibitors; C022960:2-oxindole; D018844:Cyclin-Dependent Kinases