Classification of Aurora B Kinase Inhibitors Using Computational Models
Ruizi Liu, Xianglei Nie, Min Zhong, Xiaoli Hou, Shouyi Xuan and Aixia Yan
Affiliation: State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, P.O. Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, P.R. China.
Using Self-Organizing Map (SOM) and Support Vector Machine (SVM), four classification models were built
to predict whether a compound is an active or weakly active inhibitor of Aurora B kinase. A dataset of 679 Aurora B
kinase inhibitors was collected, and randomly split into a training set (278 active and 204 weakly active inhibitors) and a
test set (109 active and 88 weakly active inhibitors). Based on 19 selected ADRIANA.Code descriptors and 135 MACCS
fingerprints, all the four models showed a good prediction accuracy of over 87% on the test set. It benefited from the
advantages of two different types of molecular descriptors in encoding structure information of compounds and
characterizing the diversity of different inhibitors. Some molecular properties, such as hydrogen-bonding interactions and
atom charge related descriptors were found to be important to the bioactivity of Aurora B kinase inhibitors.
Keywords: ADRIANA.Code descriptors, Aurora B kinase inhibitors, classification model, MACCS fingerprints, Self-
Organizing Map (SOM), Support Vector Machine (SVM).
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