Quantitative Structure–Activity Relationship Analysis of Selective Rho Kinase Inhibitors as Neuro-regenerator agent

Abstract

Introduction: Understanding the role of Rho (serine/threonine) kinases in the treatment of neurological segments, attempts have been made to find potent inhibitors of Rho enzyme by 2D quantitative structure activity relationship (QSAR) model. Materials and Methods: QSAR studies were executed on urea based scaffolds from anilines and benzylamines analogs, which were aligned for generation of chemometric based model. Multivariate statistical approaches have been applied includes linear and non-linear analysis such as multiple linear regression, partial least square and artificial neural network for the generation of model, and also an application of ADME studies was performed to ascertain the novelty and drug like properties of the intended molecules. Results: Ligand based analysis have been implemented and having excellent statistical relevance such as S-value= 0.38, F-value= 48.41, r= 0.95, r2= 0.91 and r2cv= 0.86. Five illuminating variables, VAMP polarization YY component (whole molecule), VAMP dipole Y component (whole molecule), VAMP dipole Z component (whole molecule), Kier ChiV6 path index (whole molecule) and Moment of inertia 2 size (whole molecule) were found and own profound influence on the potency of the compounds. Discussion and Conclusion: The values of standard statistical parameters reveal the predictive power and robustness of this model and also provided valuable insight to the significance of five descriptors. The acquired physicochemical properties (electronic, topological and steric) shows the important structural features required for activity against Rho kinase. After performing Lipinski’s rule of five on urea based derivatives no molecule were violating the rule. So, these features can be effectively un co rre cte d p roo f employed for modeling and screening of active neurological agents as novel RhoKinase inhibitors.