A comparative study of support vector machine, artificial neural network and Bayesian classifier for mutagenicity prediction

Mutagenicity is the capability of a chemical to carry out mutations in genetic material of an organism. In order to curtail expensive drug failures due to mutagenicity found in late development or even in clinical trials, it is crucial to determine potential mutagenicity problems as early as possible. In this work we have proposed three different classifiers, i.e. Support Vector Machine (SVM), Artificial Neural Network (ANN) and Bayesian classifiers, for the prediction of mutagenicity of compounds based on seventeen descriptors. Among the three classifiers Radial Basis Function (RBF) kernel based SVM classifier appeared to be more accurate for classifying the compounds under study on mutagens and non-mutagens. The overall prediction accuracy of SVM model was found to be 71.73% which was appreciably higher than the accuracy of ANN based classifier (59.72%) and Bayesian classifier (66.61%). It suggests that SVM based prediction model can be used for predicting mutagenicity more accurately compared to ANN and Bayesian classifier for data under consideration.