Pharmacophore and QSAR Modeling of Endothelial Nitric Oxide Synthase Inhibitors and Subsequent Validation and In Silico Search for New Hits

Endothelial Nitric Oxide synthase (eNOS) has an emerging role in chronic inflammation and cancerthus prompting continuous attempts to discover new inhibitors of this enzyme. Towards this end,efforts to discover and optimize new eNOS inhibitors are essential. Therefore, we explored thepharmacophoric space of 151 eNOS inhibitors using ten diverse sets of inhibitors to identify highquality pharmacophores. Subsequently, genetic algorithm and multiple linear regression analysiswere employed to select an optimal combination of pharmacophoric models and 2D physicochemicaldescriptors capable of accessing a self-consistent quantitative structure-activity relationship (QSAR)of optimal predictive potential (r2121 = 0.77, F = 63.5, r2LOO = 0.62, and r2PRESS against 30 external testinhibitors = 0.63). Interestingly, only one pharmacophore emerged in the optimal QSAR equation.Comparisons with the binding site of eNOS and receiver-operating characteristic (ROC) curvesanalysis established the validity of this QSAR-selected pharmacophore model. We employed thepharmacophoric model and associated QSAR equation to screen the national cancer institute list ofcompounds (NCI).