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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,  r  2  = 0.928–0.988, = 0.894–0.954, RMSE = 0.002–0.412,  s  = 0.001–0.214), and the predicted p K  i  values by SVM-Score were found to be in good agreement with the observed values for the training samples ( n  = 24,  r  2  = 0.967, = 0.899, RMSE = 0.295,  s  = 0.170) and test samples ( n  = 13,  q  2  = 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.

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