Introducing a pyrazolopyrimidine as a multi-tyrosine kinase inhibitor, using multi-QSAR and docking methods

In cancer disease, which is one of the problems of today's human societies, the expression of some tyrosine kinase receptors that are effective in the growth and proliferation of cancerous cells rises. Therefore, it is essential to develop and propose new drugs to target the receptors. Performing modeling calculations such as QSAR and docking makes the drug discovery process more efficient. Thus, backpropagation artificial neural network was used for multidimensional quantitative structure-activity relationship (QSAR) to identify essential features of pyrazolopyrimidine moiety, responsible for anticancer activity. The statistical parameters of the model show that multi-QSAR has sufficient validity and accuracy. According to the QSAR modeling, among 26 compounds, the interaction of eight candidates with EGFR, FGFR4, PDGFRA, and VEGFR2 was analyzed by docking modeling. The results showed that 1u compound binds to proteins in a more appropriate area (except FGFR4) with acceptable energy. The results of docking for VEGFR2 binding showed that 1u binds to the active site and binding site of receptor, and it was in the interaction with ten residues in the sites. Although the binding site of 1u molecule in the FGFR4 was not suitable, the binding free energy was excellent (- 9.22 kcal mol^-1), which was less than those two anticancer drugs of gefitinib and regorafenib. Furthermore, the values of binding free energy were - 8.69, - 9.64, and - 9.19 kcal mol^-1 for EGFR, PDGFRA, and VEGFR2, respectively. Therefore, this study introduces 1u as an anticancer agent that can inhibit the tyrosine kinase receptors.