Background:
Alzheimer’s disease (AD) is increasingly being recognized as one of the
lethal diseases in older people. Acetylcholinesterase (AChE) has proven to be the most promising
target in AD, used for designing drugs against AD.
Methods:
In silico studies, 2D- or 3D-QSAR like hologram QSAR (HQSAR), Topomer
comparative molecular field analysis (Topomer CoMFA), comparative molecular field analysis
(CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods were used to
generate QSAR models for acetylcholinesterase inhibitors.
Results:
Acetylcholinesterase inhibitors used for the present study contain a series of 7-
hydroxycoumarin derivatives connected by piperidine, piperazine, tacrine, triazole, or benzyl
fragments through alkyl or amide spacer training set compounds were used to generate best model
with a HQSAR q2 value of 0.916 and r2 value of 0.940; a Topomer CoMFA q2 value of 0.907 and r2
value of 0.959, CoMFA q2 value of 0.880 and r2 value of 0.960; and a CoMSIA q2 value of 0.865
and r2 value of 0.941. In addition, contour plots obtained from QSAR models suggested the
significant regions that influenced the AChE inhibitory activity.
Conclusion:
In light of these results, this study provides knowledge about the structural
requirements for the development of more active acetylcholinesterase inhibitors. In addition, the
predicted ADME profile helps us to find CNS active molecules, the obtained prediction compared
with well-known AChE inhibitors viz., ensaculin, tacrine, galantamine, rivastigmine, and donepezil.
Based on the knowledge obtained from these studies, the hybridization approach is one of the best
ways to find lead compounds and these findings can be useful in the treatment of Alzheimer's
disease.
Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-driven in silico Catalyst Optimization
