Discriminating of ATP competitive Src kinase inhibitors and decoys using self-organizing map and support vector machine.

A data set containing 686 Src kinase inhibitors and 1,941 Src kinase non-binding decoys was collected and used to build two classification models to distinguish inhibitors from decoys. The data set was randomly split into a training set (458 inhibitors and 972 decoys) and a test set (228 inhibitors and 969 decoys). Each molecule was represented by five global molecular descriptors and 18 2D property autocorrelation descriptors calculated using the program ADRIANA.Code. Two machine learning methods, a Kohonen's self-organizing map (SOM) and a support vector machine (SVM), were utilized for the training and classification. For the test set, classification accuracy (ACC) of 99.92% and Matthews correlation coefficient (MCC) of 0.98 were achieved for the SOM model; ACC of 99.33% and MCC of 0.98 were obtained for the SVM model. Some molecular properties, such as molecular weight, number of atoms in a molecule, hydrogen bond properties, polarizabilities, electronegativities, and hydrophobicities, were found to be important for the inhibition of Src kinase.