Computational modeling and design of renin inhibitors

Abstract

The recently introduced field-based QSAR was employed to develop robust quantitative 3D QSAR models to comprehend the activity of several structurally diverse classes of small molecule renin inhibitors reported in literature. A reasonable predictive model with an r2 (pred) of ∼0.67 and rmse of 0.79 was achieved for an external validation set of ∼150 compounds centered on the model developed using ∼450 training set compounds. Based on the developed 3D QSAR models and additional insights gained from reported X-ray structures, opportunity for activity improvements in the [aza]indole scaffold was explored using a carefully designed virtual library of ∼2300 compounds. The potential for success of such combined structure-guided and ligand-based approach was justified when the resulting prediction was compared against a representative with supporting experimental results.