New molecular scaffolds for the design of Alzheimer's acetylcholinesterase inhibitors identified using ligand- and receptor-based virtual screening

The identification of important chemical features of acetylcholinesterase (AChE) inhibitors will be helpful to discover the potent candidate to inhibit the AChE activity. The best hypothesis from structure-based, Hypo1, one hydrophobic (H) pointed toward ILE444, TRP84, three hydrogen bond accepter (HBA), two hydrogen bond donor, one positive ionizable toward TRP84, PHE330, and 11 excluded volume sphere, were generated using LigandScout. Test and decoy sets were used to corroborate the best hypotheses, and the validated hypotheses were used to screen the Maybridge database. Only 14 compounds were prioritized as promising hits. The quantitative structure-activity relationship (QSAR) equation was developed based on 44 AChE inhibitors: 34 training set compounds and 10 test set compounds. The model was developed using five information-rich descriptors-HBA, log P, HOF, EE, and dipole-playing an important role in determining AChE inhibitory activity. QSAR model (model 3) yielded good statistical data, r (2) = 0.723; q (2) = 0.703; n = 34 for training set. This model was further validated using leave-one-out cross-validation approach, Fischer statistics (F), Y randomisation test, and prediction based on the test data set. Molecular docking of 44 1-indanone derivatives & screened hits compounds were performed to identify the binding residue in AChE. Finally, the screened hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance AChE binding affinity for Alzheimer disease.