Structure–activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines
2006; Elsevier BV; Volume: 581; Issue: 2 Linguagem: Inglês
10.1016/j.aca.2006.08.031
ISSN1873-4324
AutoresJiazhong Li, Huanxiang Liu, Xiaojun Yao, Mancang Liu, Zhide Hu, Botao Fan,
Tópico(s)Multicomponent Synthesis of Heterocycles
ResumoThe 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.
Referência(s)