Pharmacophore and QSAR modeling of neuronal nitric oxide synthase ligands and subsequent validation and in silico search for new scaffolds

Abstract

Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several structure-based studies were conducted to search novel selective nNOS inhibitors. Objective: Discovery of novel nNOS lead scaffolds through the integration of ligand-based threedimensional (3D) pharmacophore (s) with quantitative structure-activity relationship model. Method: The pharmacophoric space of ten structurally diverse sets acquired from 145 previously reported nNOS inhibitors was scrutinize to fabricate representative pharmacophores. Afterwards, genetic algorithm together with multiple linear regression analysis was applied to find out an optimal pharmacophoric models and 2D physicochemical descriptors able to produce optimal predictive QSAR equation (r1162 =0.76, F = 353, rLOO2 = 0.69, r2 PRESS against 29 external test ligands =0.51). A minimum of three binding modes between ligands and nNOS binding pocket rationalized by the emergence of three pharmacophoric models in the QSAR equation were illustrated. The QSAR-selected pharmacophores were validated by receiver operating characteristic curves analysis and afterward invested as a tool for screening national cancer institute (NCI) database. Results: Low micro molar novel nNOS inhibitors were revealed. Conclusion: Two structurally diverse compounds 148 and 153 demonstrated new scaffolds toward the discovery of potent nNOS inhibitors.