3D-QSAR and virtual screening studies in identification of new Rho kinase inhibitors with different scaffolds

Abstract

Among the most well-known members of the serine/threonine (Ser/Thr) protein kinase family are Rho kinases. They are potential targets to treat some diseases, such as cancer, cardiovascular diseases and multiple sclerosis, because of their key roles in the organization of the actin cytoskeleton. In this study, 3D-QSAR (quantitative structure–activity relationship) techniques using popular CoMFA and CoMSIA tools as well as recently developed automated grid potential analysis (AutoGPA) based on pharmacophore alignment were applied on 41 urea-based derivatives as Rho kinase inhibitors, which were split into 32 and 9 compounds as train and test sets using Kennard and Stone algorithm. The statistical parameters of AutoGPA-based 3D-QSAR model were q 2 = 0.506, $$R_{\text{pred}}^{ 2}$$ = 0.844, $$R_{\text{m}}^{2}$$ = 0.806 which show its acceptable prediction reliability. The verified model was further utilized to search novel hits from ZINC database in virtual screening task. The obtained compounds were subjected to Lipinski filter, then their activities were predicted by AutoGPA model and docked with CDOCKER algorithm to discover potent hits. In silico ADME and toxicity risk assessment analyses were carried out on the seven hits with highest CDOCKER scores. Six out of the seven hits have diverse structures and are reported as new scaffold candidates for design of new Rho kinase inhibitors.