Affiliation: Department of Chemistry, Capital Normal University, Beijing 100048, China.
Direct renin inhibitors (DRIs) have increasingly shown a significant advantage in the treatment of hypertension and protection of target organs. In this paper, a series of azaindole class renin inhibitors were subjected to 3D-QSAR study using Topomer CoMFA. Five kinds of splitting mode for different fragment cutting and 6 different training and test sets grouping were attempted to build consensus models. The results indicated that 6 consensus models had similar predictability (q2 and r2pred) and stability (r2). The best model showed good stability and predictability (q2 of 0.616, r2 of 0.908). The r2pred value of the external test set was 0.70, which means that the model had an excellent external predictive ability, and the robustness of the developed model was assessed by the Y-randomization test. This study also adopted the methodology of fragment-based drug design (FBDD) to virtual screen new renin inhibitors by using Topomer Search technology. The R1-group of the compound No. 13 with the highest activity was chosen as the basic scaffold, and its remaining R2-group acted as a query to screen 142,025 molecules of ZINC database for similar fragments. The obtained 30 fragments with the highest R2-group contribution values were added to the basic scaffold respectively. Finally 30 new azaindole compounds with potent high activities were obtained. Further the binding modes were studied by using Surflex- Dock. The docking results showed good binding interactions of the designed compounds with the renin protein, thus the rationality of this design was further verified from the perspective of the renin receptor.