Background:
Acetylcholinesterase (AChE), a serine hydrolase, is an important drug
target in the treatment of Alzheimer's disease (AD). Thus, novel AChE inhibitors were designed
and developed as potential drug candidates, for significant therapy of AD.
Objective:
In this work, molecular modeling studies, including CoMFA, CoMFA-RF, CoMSIA,
HQSAR and molecular docking and molecular dynamics simulations were performed on a series
of AChE inhibitors to get more potent anti-Alzheimer drugs.
Methods:
2D/3D-QSAR models including CoMFA, CoMFA-RF, CoMSIA, and HQSAR methods
were carried out on 40 pyrimidinylthiourea derivatives as data set by the Sybylx1.2 program. Molecular
docking and molecular dynamics simulations were performed using the MOE software and
the Sybyl program, respectively. Partial least squares (PLS) model as descriptors was used for
QSAR model generation.
Results:
The CoMFA (q2, 0.629; r2ncv, 0.901; r2pred, 0.773), CoMFA-RF (q2, 0.775; r2ncv, 0.910; r2pred, 0.824), CoMSIA (q2, 0.754; r2ncv, 0.919; r2pred, 0.874) and HQSAR models (q2, 0.823; r2ncv, 0.976; r2pred, 0.854) for training and test set yielded significant statistical results.
Conclusion:
These QSAR models were excellent, robust and had good predictive capability. Contour
maps obtained from the QSAR models were validated by molecular dynamics simulationassisted
molecular docking study. The resulted QSAR models could be useful for the rational design
of novel potent AChE inhibitors in Alzheimer's treatment.
Combinatorial peptide library screening for discovery of diverse α-glucosidase inhibitors using molecular dynamics simulations and binary QSAR models
