Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis

Two 3D quantitative structure⁻activity relationships (3D-QSAR) models for predicting Cannabinoid receptor 1 and 2 (CB₁ and CB₂) ligands have been produced by way of creating a practical tool for the drug-design and optimization of CB₁ and CB₂ ligands. A set of 312 molecules have been used to build the model for the CB₁ receptor, and a set of 187 molecules for the CB₂ receptor. All of the molecules were recovered from the literature among those possessing measured Ki values, and Forge was used as software. The present model shows high and robust predictive potential, confirmed by the quality of the statistical analysis, and an adequate descriptive capability. A visual understanding of the hydrophobic, electrostatic, and shaping features highlighting the principal interactions for the CB₁ and CB₂ ligands was achieved with the construction of 3D maps. The predictive capabilities of the model were then used for a scaffold-hopping study of two selected compounds, with the generation of a library of new compounds with high affinity for the two receptors. Herein, we report two new 3D-QSAR models that comprehend a large number of chemically different CB₁ and CB₂ ligands and well account for the individual ligand affinities. These features will facilitate the recognition of new potent and selective molecules for CB₁ and CB₂ receptors.