Noncovalent inhibitors of protein kinases have different modes of action. They bind to the active or inactive form of kinases, compete with ATP, stabilize inactive kinase conformations, or act through allosteric sites. Accordingly, kinase inhibitors have been classified on the basis of different binding modes. For medicinal chemistry, it would be very useful to derive mechanistic hypotheses for newly discovered inhibitors. Therefore, we have applied different machine learning approaches to generate models for predicting different classes of kinase inhibitors including types I, I1/2, and II as well as allosteric inhibitors. These models were built on the basis of compounds with binding modes confirmed by X-ray crystallography and yielded unexpectedly accurate and stable predictions without the need for deep learning. The results indicate that the new machine learning models have considerable potential for practical applications. Therefore, our data sets and models are made freely available.