Discovery of potent anticonvulsant ligands as dual NMDA and AMPA receptors antagonists by molecular modelling studies

3D-QSAR and Molecular Docking Studies on Oxadiazole Substituted Benzimidazole Derivatives: Validation of Experimental Inhibitory Potencies Towards COX-2

Background:

In past few decades, computational chemistry has seen significant advancements in design and development of novel therapeutics. Benzimidazole derivatives showed promising anti-inflammatory activity through the inhibition of COX-2 enzyme.

Objective:

The structural features necessary for COX-2 inhibitory activity for a series of oxadiazole substituted benzimidazoles were explored through 3D-QSAR, combinatorial library generation (Combi Lab) and molecular docking.

Methods:

3D-QSAR (using kNN-MFA (SW-FB) and PLSR (GA) methods) and Combi Lab studies were performed by using VLife MDS Molecular Design Suite. The molecular docking study was performed by using AutoDockVina.

Results:

Significant QSAR models generated by PLSR exhibited r2 = 0.79, q2 = 0.68 and pred_r2 = 0. 84 values whereas kNN showed q2 = 0.71 and pred_r2 = 0.84. External validation of developed models by various parameters assures their reliability and predictive efficacy. A library of 72 compounds was generated by combinatorial technique in which 11 compounds (A1-A5 and B1-B6) showed better predicted biological activity than the most active compound 27 (pIC50 = 7.22) from the dataset. These compounds showed proximal interaction with amino acid residues like TYR355 and/or ARG120 on COX-2(PDB ID: 4RS0).

Conclusion:

The present work resulted in the design of more potent benzimidazoles as COX-2 inhibitors with good interaction as compared to reference ligand. The results of the study may be helpful in the development of novel COX-2 inhibitors for inflammatory disorders.

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r² = 0.928–0.988,  = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pK_i values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r² = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q² = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.