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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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