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    In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential. © 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.


    Sowmya Andole, Husna Sd, Srija Sudhula, Lavanya Vislavath, Hemanth Kumar Boyina, Kiran Gangarapu, Vasudha Bakshi, Krishna Prasad Devarakonda. 3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors. Advances in experimental medicine and biology. 2023;1424:233-240

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    PMID: 37486499

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