Support vectors selection

Key Words 2D-QSAR traditional QSAR 3D-QSAR nD-QSAR 4D-QSAR receptor-independent QSAR receptor-dependent QSAR high throughput screening alignment conformation chemometrics principal components analysis partial least squares artificial neural networks support vector machines Binary-QSAR selecting QSAR descriptors. 

Yabuki, Y., Muramatsu, T., Hirokawa, T., Mukai, H. and Suwa, M. (2005) GRIFFIN a system for predicting GPCR-G-protein coupling selectivity using support vector machines... 

Histone deacetylases (HDACs) play a critical role in transcription regulation. Small molecule HDAC inhibitors have become an emerging target for the treatment of cancer and other cell proliferation diseases. We have employed variable selection k nearest neighbor approach (iNN)and support vector machines (SVM) approach to generate QSAR models for 59 chemically diverse... 

Support Vector Machine (SVM) is a classification and regression method developed by Vapnik.30 In support vector regression (SVR), the input variables are first mapped into a higher dimensional feature space by the use of a kernel function, and then a linear model is constructed in this feature space. The kernel functions often used in SVM include linear, polynomial, radial basis function (RBF), and sigmoid function. The generalization performance of SVM depends on the selection of several internal parameters of the algorithm (C and e), the type of kernel, and the parameters of the kernel.31... 

Support vector machines In addition to more traditional classification methods like clustering or partitioning, other computational approaches have recently also become popular in chemoinformatics and support vector machines (SVMs) (Warmuth el al. 2003) are discussed here as an example. Typically, SVMs are applied as classifiers for binary property predictions, for example, to distinguish active from inactive compounds. Initially, a set of descriptors is selected and training set molecules are represented as vectors based on their calculated descriptor values. Then linear combinations of training set vectors are calculated to construct a hyperplane in descriptor space that best separates active and inactive compounds, as illustrated in Figure 1.9. 

Xue Y, Li ZR, Yap CW, Sun I.Z, Chen X, Chen YZ. Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents. J Chem Inf Comput Sci 2004 44 1630-8. 

Newer data analysis methods overcome the difficulties that small sample-to-variabies ratios create for traditional statistical methods. These new methods fall into two major categories (1) support-vector classification and regression methods, and (2) feature selection and construction techniques, The former are effectively determined by only a small portion of the training data (sample), while the latter select only a small subset of variables such that the available sample is enough for traditional and newer classification techniques. 

Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 2002 46 389-422. 

Norinder, U. (2003) Support vector machine models in drug design -applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing, 55, 337-346. 

Structural similarity. Such relationships are illustrated in Figure 11.8 that shotvs the variety of SSRs covered by one exemplary series of selective compounds. Despite the complexity of SSRs, in benchmark calculations, 2D fingerprints and support vector machines were successfully applied to identify target-selective compounds and significantly enrich selective compounds over nonselective and inactive molecules [55-58]. Thus, an interesting question has been whether or not this type of selectivity searching might also succeed in practical LEVS applications. 

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