Validation of protein-based alignment in 3D quantitative structure-activity relationships with CoMFA models.

The predictive capabilities of protein-based alignment (PBA) and structure-based alignment (SBA) comparative molecular field analysis (CoMFA) models have been compared. 3D quantitative structure-activity relationship (3D QSAR) models have been derived for a series of N-benzylpiperidine derivatives which are potent acetylcholinesterase (AChE) inhibitors interesting for Alzheimer's disease. To establish a comparison with the classical SBA procedure, different assay models were derived by superposing ligand conformers that are docked to the AChE active site and by using the most active compound as the reference one. A Kohonen self organizing map (SOM) was applied to analyse the molecular diversity of the test set relative to that of the training set, in order to explain the influence of molecular diversity on the predictive power of the considered models. SBA 3D QSAR models have to be used to predict the inhibitory activity only for compounds belonging to subgroups included in the training set. The PBA 3D QSAR models appeared to have a higher predictability, even for compounds with a molecular diversity greater than that of the training set. This results from the fact that the protein helps to automatically select the active conformation which is fitting the 3D QSAR model.