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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. March 2003, Volume 2, Number 3, 195-208 |
Aquatic Toxicity Prediction for Polar and Nonpolar
Narcotic Pollutants with Support Vector Machines
Ovidiu Ivanciuc
Internet Electron. J. Mol. Des. 2003, 2, 195-208
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Abstract:
Narcotic pollutants, that act by nonspecifically disrupting the
functioning of cell membranes, are categorized as polar and
nonpolar compounds. The toxicity prediction of narcotic
pollutants with QSAR (quantitative structure-activity
relationships) depends on the reliable determination of the
mechanism of toxic action. The classification of the chemical
compounds as polar and nonpolar narcotic pollutants based
on structural characteristics is of utmost importance in
predicting the aquatic toxicity for new chemicals. Support
vector machine (SVM) is a new machine learning algorithm
that proved to be reliable in the classification of organic and
bioorganic compounds. In this study we have investigated the
application of SVM for the classification of 190 narcotic
pollutants (76 polar and 114 nonpolar). Using an efficient
descriptor selection algorithm, the energy of the highest
occupied molecular orbital, the energy of the lowest
unoccupied molecular orbital, and the most negative partial
charge on any non-hydrogen atom in the molecule, all
computed with the AM1 method, were found to be necessary
for the discrimination of the polar and nonpolar compounds.
The prediction power of each SVM model was evaluated
with a leave-20%-out cross-validation procedure. The
classification performances of SVM models generated with
the dot, polynomial, radial basis function, neural, and anova
kernels, show that the statistical performances of SVM
depend strongly on the kernel type and various parameters
that control the kernel shape. An SVM model obtained with
the anova kernel offered the best results, with three errors in
calibration and four errors in prediction, all for nonpolar
chemicals. SVM is a powerful and flexible classification
algorithm, with many potential applications in molecular
design, optimization of chemical libraries, and QSAR. In the
present study we have demonstrated such an application for
the identification of the aquatic toxicity mechanism.
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