| View Larger Image | Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines [An article from: Analytica Chimica Acta] | Digitalby J. Li (Author), H. Liu (Author), X. Yao (Author), M. Liu (Author), Z. Hu (Author), B. Fan (Author)
| List Price: | $10.95 | | | Available: | Available for download now |
| | Binding: | Digital | | Publisher: | Elsevier | | Publication Date: | January 09, 2007 |
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Product Description
This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in 2007. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.Description: 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. |
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