The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.

The present work focuses on the study of the three-dimensional (3D) structural requirements for selective antagonist activity of arylpyrazole compounds at the cannabinoid CB1 and CB2 receptors. Initially, a combined high-resolution two-dimensional (2D) NMR and computer modeling approach was carried out to study the solution structure of the key pyrazole derivative N-(piperidin-1-yl)-5-phenyl-1-(n-pentyl)-4-methyl-1H-pyrazole-3-carboxamide (AM263). By using the NMR-determined molecular conformers as templates, the 3D quantitative structure-activity relationship (QSAR) studies were performed with the comparative molecular field analysis (CoMFA) approach on a set of arylpyrazole cannabinoid receptor antagonists. Molecular alignments suitable for deriving valuable pharmacophoric features for this series of compounds were determined. Such systematic 3D-QSAR/CoMFA analyses of 29 molecules and their receptor affinities gave guidance for understanding the binding affinities of arylpyrazoles at the CB1 and CB2 binding sites, respectively. Comparison of CoMFA steric and potential contour maps for affinity at the two cannabinoid receptor subtypes helps to differentiate structural requirements for each subtype and serves as a basis for the design of later-generation analogues.