Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors

The excessive activity of acetylcholinesterase enzyme AChE causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration FDA approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer's disease AD these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute NCI containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword "indol"were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship QSAR models. We then performed docking, molecular dynamics MD simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Keywords:

Binary QSAR modeling, Alzheimer's disease, virtual screening, AChE, text mining, molecular docking molecular dynamics simulations, computational drug design,

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