title: 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 r(2) (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.
authors.affiliation: Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, 1901 East Road, Houston, TX 77054, USA. gsubramanian@mdanderson.org
authors.forename: Govindan
authors.initials: G
authors.lastname: Subramanian
mesh_terms: D020134:Catalytic Domain; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D015195:Drug Design; D004791:Enzyme Inhibitors / Q000138:chemical synthesis / Q000737:chemistry*; D020128:Inhibitory Concentration 50; D008024:Ligands; D008958:Models, Molecular; D021281:Quantitative Structure-Activity Relationship; D012083:Renin / Q000737:chemistry; D000092502:Renin Inhibitors
chemical_list: D004791:Enzyme Inhibitors; D008024:Ligands; D012083:Renin; D000092502:Renin Inhibitors