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Prediction of biological activity of Aurora-A kinase inhibitors by multilinear regression analysis and support vector
Aixia Yan1, Yang Chong, Liyu Wang
1State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, PO Box 53, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, Beijing 100029, PR China. yanax@mail.buct.edu.cn
Abstract:
Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen's self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.
