Classification of Aurora kinase inhibitors by self-organizing map (SOM) and support vector machine (SVM)

The Aurora kinase family (consisting of Aurora-A, -B and -C) is an important group of enzymes that controls several aspects of cell division in mammalian cells. In this study, 512 compounds of Aurora-A and -B inhibitors were collected. They were classified into three classes: dual Aurora-A and Aurora-B inhibitors, selective inhibitors of Aurora-A and selective inhibitors of Aurora-B by Self-Organizing Map (SOM) and Support Vector Machine (SVM). The prediction accuracies of the models (based on the training/test set splitting using SOM method) for the test set were 92.2% for SOM1 and 93.8% for SVM1, respectively. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and structure–activity relationship of Aurora kinase inhibitors was summarized, which may be helpful to find the important structural features of inhibitors relating to the selectivity to Aurora kinases.

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► A dataset of 512 Aurora-A and Aurora-B inhibitors was collected.
► 15 descriptors were selected by correlation analysis method.
► Two models were built by SOM and SVM for predicting the selectivity of inhibitors.
► The extended connectivity fingerprints of inhibitors were calculated and analyzed.
► Structure–activity relationship of Aurora kinase inhibitors was summarized.