De novo design and virtual screening of potential Bcr-Abl tyrosine kinase inhibitors using deep learning and molecular modeling technologies

Abstract

De novo design and virtual screening of small-molecule compounds with a high potential inhibitory activity against the Bcr-Abl tyrosine kinase playing a key role in the pathogenesis of chronic myeloid leukemia (CML) were carried out by an integrated computational approach including technologies of deep learning and molecular modeling. As a result, according to the calculation data we identified 5 compounds exhibiting low values of binding free energy to the enzyme comparable with those predicted for imatinib, nilotinib and ponatinib, anticancer drugs widely used in the clinic to treat patients with CML. It was shown that these compounds are able to form stable complexes with the ATP-binding sites of the Bcr-Abl tyrosine kinase and its mutant form T315I, which is confirmed by the analysis of the profiles of binding affinity and intermolecular interactions responsible for their energy stabilization. Based on the obtained data, these compounds, which have been generated by the deep learning neural network, are assumed to form promising basic structures for development of new effective drugs for treatment of patients with CML.