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Identification of novel acetylcholinesterase inhibitors through 3D-QSAR, molecular docking, and molecular dynamics simulation targeting Alzheimer’s disease

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Abstract

Acetylcholinesterase (AChE) is a potential target for the development of small molecules as inhibitors for the therapy of Alzheimer’s disease (AD). To design highly active acetylcholinesterase inhibitors, a three-dimensional quantitative structure–activity relationship (3D-QSAR) approach was performed on a series of N-benzylpyrrolidine derivatives previously evaluated for acetylcholinesterase inhibitory activity. The developed two models, CoMFA and CoMSIA, were statistically validated, and good predictability was achieved for both models. The information generated from 3D-QSAR contour maps may provide a better understanding of the structural features required for acetylcholinesterase inhibition and help to design new potential anti-acetylcholinesterase molecules. Consequently, six novel acetylcholinesterase inhibitors were designed, among which compound A1 with the highest predicted activity was subjected to detailed molecular docking and compared to the most active compound. Extra-precision molecular dynamics (MD) simulation of 50 ns and binding free energy calculations using MM-GBSA were performed for the selected compounds to validate the stability. These results may afford important structural insights needed to identify novel acetylcholinesterase inhibitors and other promising strategies in drug discovery.

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Acknowledgements

The authors are grateful to the “Association Marocaine des Chimistes Théoriciens” (AMCT) for its technical support.

Author information

Authors and Affiliations

  1. Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University of Moulay Ismail, Meknes, Morocco

    Khalil El Khatabi, Reda El-Mernissi, Ilham Aanouz, Mohammed Aziz Ajana, Tahar Lakhlifi & Mohammed Bouachrine

  2. Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, People’s Republic of China

    Abbas Khan & Dong-Qing Wei

  3. State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center On Antibacterial Resistances, Joint Laboratory of International Laboratory of Metabolic and Developmental Sciences, Ministry of Education and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, People’s Republic of China

    Dong-Qing Wei

  4. Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nashan District, Shenzhen, Guangdong, 518055, People’s Republic of China

    Dong-Qing Wei

  5. EST Khenifra, Sultan Moulay Sliman University, Beni Mellal, Morocco

    Mohammed Bouachrine

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  1. Khalil El Khatabi
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  2. Reda El-Mernissi
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Contributions

KE: draft preparation, data handling, data analysis, writing, and reviewing. RE: conceptualization and data handling. IA: data handling and reviewing. MAA: data analysis, study justification, and supervision. TL: supervision and project administration. AK: data analysis and writing. DQW: writing and reviewing. MB: study justification and supervision.

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Correspondence to Khalil El Khatabi.

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El Khatabi, K., El-Mernissi, R., Aanouz, I. et al. Identification of novel acetylcholinesterase inhibitors through 3D-QSAR, molecular docking, and molecular dynamics simulation targeting Alzheimer’s disease. J Mol Model 27, 302 (2021). https://doi.org/10.1007/s00894-021-04928-5

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