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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. August 2007, Volume 6, Number 8, 229-236

Support Vector Machines QSAR for the Toxicity of Organic Chemicals to Chlorella vulgaris with SVM Parameters Optimized with Simplex
Zhong-Sheng Yi and Li-Tang Qin
Internet Electron. J. Mol. Des. 2007, 6, 229-236

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Abstract:
The key to a successful application of support vector machines (SVM) is to selecte proper parameters, but there is no general method for selecting the best set of SVM parameters. The predictive power of SVM models depends strongly on the set of parameters that control the model. In this paper we used the simplex optimization method to search for the optimum set of SVM parameters, namely the capacity parameter C, the insensitive loss parameter ε and the parameter γ that controls the shape of the RBF kernel. The leave-one-out cross-validation correlation coefficient q2 is used as objective function for the simplex optimization of SVM parameters. SVM quantitative structure-activity relationships (QSAR) models were built for the toxicity of organic chemicals to Chlorella vulgaris. The SVM models with simplex optimized parameters are compared with multi-linear regression QSAR models obtained in the same conditions. A series of QSAR models with one to three variables were obtained for the acute toxicity of 91 organic chemicals to Chlorella vulgaris. The SVM models with parameters optimized with simplex have better statistics than the multi-linear regression QSAR equations. The results from the present investigation demonstrate that the simplex algorithm is an efficient approach in finding the best set of SVM parameters for QSAR models.

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