Classification of Aurora-A Kinase Inhibitors Using Self-Organizing Map (SOM) and Support Vector Machine (SVM)

Two classification models of 148 Aurora-A kinase inhibitors were developed to separate active and weakly potent active inhibitors of Aurora-A kinase. Each molecule was represented by 12 selected molecular descriptors calculated by the ADRIANA.Code. Then the classification models were built using a Kohonen's Self-Organizing Map (SOM) and a Support Vector Machine (SVM) method, respectively, which could be used for virtual screening an existing database to find possible new lead compounds with higher activity. The prediction accuracy of the models for the training and test sets are 96.6 % and 90.0 % for SOM, 93.2 % and 93.3 % for SVM.