Pharmacophore-based drug design for the identification of novel butyrylcholinesterase inhibitors against Alzheimer's disease

Background: Alzheimer's disease is a severe neurodegenerative disease of the central nervous system in the elderly.

Hypothesis/purpose: In our study, we aimed to find the best potential small molecule for AD treatment.

Study design: We used many models in Discovery Studio 2016 to find new potential inhibitors of butyrylcholinesterase (BChE), including pharmacophore model, virtual screening model, molecular docking model, de novo evolution model.

Methods: Ligand-based pharmacophore models were used to identify the critical chemical features of BChE inhibitors using the module of 3D QSAR Pharmacophore Generation in Discovery Studio 2016. The best pharmacophore model was then validated by cost analysis, Fischer's randomization method, 3D-QSAR Method of the training set and test set. The compounds that match the best pharmacophore model with the predicted activity <1 μM filtered by Lipinski's rule of five were subjected to molecular docking.

Result: After virtual screening, 35 compounds filtered by Lipinski's rule of five and ADMET analysis were subjected to molecular docking and then the number were narrowed down on 10 compounds based on -CDOCKER_ENERGY. Finally, we obtained and modified the best potential candidate ENA739155.

Conclusion: Ultimately, ENA739155_Evo with -CDOCKER_ENERGY of 47.12, estimate activity of 0.012, fit value of 10.02 could be further subjected to drug development and forwarded as better alternatives to the current batch of medicines used for the treatment of AD.