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Discovery of new multifunctional selective acetylcholinesterase inhibitors: structure-based virtual screening and biological evaluation

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Abstract

Although the mechanism of Alzheimer’s disease (AD) is still not fully understood, the development of multifunctional AChE inhibitors remains a research focus for AD treatment. In this study, 48 AChE candidate inhibitors were picked out from SPECS database through a pharmacophore- and molecular docking-based virtual screening. The biological evaluation results indicated that four compounds 7, 29, 41 and 48 with different scaffolds exhibited potent and selective AChE inhibitory activity, with the best IC50 value of 1.62 ± 0.11 μM obtained for 48. Then their mechanism of action, the inhibition on Aβ aggregation, neurotoxicity, and neuroprotective activity against Aβ-induced nerve cell injury were well studied. The binding mode of 48 with AChE was also proposed. The present bioassay results indicated that these multifunctional AChE inhibitors were worth for further structural derivatization to make them the anti-AD lead compounds.

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Acknowledgements

This research work was financially supported by the National Natural Science Foundation of China [Nos. 21672082, 81803438], Shandong Key Development Project [No. 2016GSF201209], the Young Taishan Scholars Program [No. tsqn20161037], Natural Science Foundation of Shandong Province [Nos. ZR201807060857, ZR2017BH038, JQ201721], and Shandong Talents Team Cultivation Plan of University Preponderant Discipline [No. 10027].

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  1. School of Biological Science and Technology, University of Jinan, Jinan, 250022, People’s Republic of China

    Cheng-Shi Jiang, Yong-Xi Ge, Zhi-Qiang Cheng, Jia-Li Song, Yin-Yin Wang, Kongkai Zhu & Hua Zhang

  2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, People’s Republic of China

    Kongkai Zhu

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Jiang, CS., Ge, YX., Cheng, ZQ. et al. Discovery of new multifunctional selective acetylcholinesterase inhibitors: structure-based virtual screening and biological evaluation. J Comput Aided Mol Des 33, 521–530 (2019). https://doi.org/10.1007/s10822-019-00202-2

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