Abstract. Inhibition of Acetylcholinesterase (AChE) is an important approach for Alzheimer's disease (AD) treatment. Different synthetic and natural inhibitors are used for Acetylcholinesterase inhibition. Synthetic inhibitors Polyphenols (Tacrine, Donepezil, Rivastigmine, Galantamine) and Natural molecules from Green tea which mainly contains catechins (Epicatechin, Epicatechin Gallate, Epigallocatechin, Epigallocatechin Gallate) are used to inhibit Acetylcholinesterase. In this work we use molecular docking methods to identify the ligand which has the best interaction energy with AChE among synthetic and natural products, and also descript binding affinity in purpose to design new inhibitor ligands. Obtained results from Docking and analyze of complexes parameters showed that the best affinity binding was observed for both (Galantamie and Epicatechin Gallete).This latter leads to same conclusion with experimentation inhibition study. We observed also that bulky group causes conformational rearrangement in the active pocket, which will probably give better interactions.
To treat Alzheimer's Disease (AD), which is the most prevalent form of dementia, cholinesterase enzymes (AChE and BuChE) and amyloid-beta (Aβ) are attractive targets. In this work, different computational approach namely Density Functional Theory (DFT), Molecular Docking, and multi-QSAR modeling were performed on 22 donepezil-based derivatives which were reported as potent dual Aβ and (AChE and BuChE) inhibitors. The molecular geometries of the studied derivatives were carried out using GAUSSIAN 09 software with the level of theory (DFT, 6/31g*). The dual inhibitors adopted minimum energy. The results pointed out the importance of the inhibitors' geometries in enzyme inhibition. The QSAR models elaborated by means of Molecular Operating Environment (MOE) package, showed good statistical values for targets AChE (R²adj = 0.976, q2 = 0.871, RMS = 0.130), BuChE (R²adj = 0.976, q2 = 0.554, RMS = 0.092) and Aβ (R²adj = 0.861, q2 = 0.525, RMS = 0.113). To identify the binding pattern between the ligands and target enzymes, we implemented molecular docking studies for the datasets. The obtained information was related to the essential structural features that were related to the QSAR of the predicted models.
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