In an effort to develop a quantitative ligand-binding model for the CB1R, pharmacophore modelling studies were performed on 1-sulfonyl 4-acylpiperazine derivatives. Pharmacophore Alignment and Scoring Engine (PHASE) was used to develop predictive Common Pharmacophore Hypotheses (CPHs) in which a five point pharmacophore with one hydrogen bond acceptor (A), two lipophilic/hydrophobic groups (H) and two aromatic rings (R) as pharmacophoric features was developed. On the basis of statistical values, the best-fitted model was identified and the same alignment was used for CoMFA and CoMSIA studies. The models developed showed an excellent r2 predictive value of 0.879 for CoMFA and 0.764 for CoMSIA. The robustness of the models was validated and the mean activity for test set compounds can estimate external predictivity. The 3D contour maps generated from CoMFA/CoMSIA offer important structural insights and provided interpretable explanation of SAR for the compounds. The obtained results may help in designing analogs with better activity.
Keywords: CB1R, CoMFA, CoMSIA, 3D-QSAR, Endocannabinoid, Obesity, PHASE, ligand-binding model, pharmacophoric features, 3D contour maps, endocannabinoid system, anti-obesity therapeutics, metabolic homeostasis, cannabinoid CB1 inverse agonists, 1-sulfonyl-4-acylpiperazines, 3D-pharmacophore model, GPCR ligand binding modes
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