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

Khatabi, Khalil El; El-Mernissi, Reda; Aanouz, İlham; Ajana, Mohammed Aziz; Lakhlifi, Tahar; Khan, Abbas; Wei, Dong‐Qing; Bouachrine, Mohammed

doi:10.1007/s00894-021-04928-5

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…The phenyl moiety on Trp84’s left side forms a π-π stacking that is advantageous to the binding. Additionally, the neighbouring Phe290 residue positioned close to R 1 substituent and the OCF(C 2 H 5 ) 2 of the core pyrrolidine ring produce a π-sigma effect, indicating that the hydrophobic group at the R 1 substituent is responsible for compound’s improved biological activity [ 38 ].…”

Section: Inhibitionmentioning

confidence: 99%

“… General structure of pyrrolidine derivatives and compound 15 being most active, synthesized by El Khatabi et al [ 38 ]. …”

Section: Figures and Schemesmentioning

confidence: 99%

“…The following supporting information can be downloaded at: , Table S1: Compounds, reference compounds, inhibitory values and docking results [ 21 , 25 , 30 , 31 , 32 , 35 , 36 , 37 , 38 , 40 , 42 , 46 , 48 , 50 , 62 , 68 , 70 , 71 , 76 , 77 , 78 , 82 , 83 , 87 , 92 ].…”

mentioning

confidence: 99%

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A Review on Recent Approaches on Molecular Docking Studies of Novel Compounds Targeting Acetylcholinesterase in Alzheimer Disease

Peitzika

Pontiki

2023

Molecules

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Alzheimer’s disease (AD), a neurodegenerative brain disorder that affects millions of people worldwide, is characterized by memory loss and cognitive decline. Low levels of acetylcholine and abnormal levels of beta-amyloid, T protein aggregation, inflammation, and oxidative stress, have been associated with AD, and therefore, research has been oriented towards the cholinergic system and primarily on acetylcholinesterase (AChE) inhibitors. In this review, we are focusing on the discovery of AChE inhibitors using computer-based modeling and simulation techniques, covering the recent literature from 2018–2022. More specifically, the review discusses the structures of novel, potent acetylcholinesterase inhibitors and their binding mode to AChE, as well as the physicochemical requirements for the design of potential AChE inhibitors.

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Section: Inhibitionmentioning

confidence: 99%

“… General structure of pyrrolidine derivatives and compound 15 being most active, synthesized by El Khatabi et al [ 38 ]. …”

Section: Figures and Schemesmentioning

confidence: 99%

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A Review on Recent Approaches on Molecular Docking Studies of Novel Compounds Targeting Acetylcholinesterase in Alzheimer Disease

Peitzika

Pontiki

2023

Molecules

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“…The translocator comparative molecular force field method (Topomer CoMFA) is a 3D-QSAR method 21 that can predict and optimize the biological activity of compounds, proposed in 2004 by Crame et al 22 It is the product of topomer technology of the molecular descriptors combined with CoMFA. 23 The Topomer CoMFA model is easy and fast to operate during the modeling process, it can rationally establish the relationship between the structure and activity of compounds, and it accurately assesses the contribution value of each cleavage group, which is indispensable for the molecular screening work afterwards. 24…”

Section: Topomer Comfamentioning

confidence: 99%

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Guo,

Chang,

Tong

et al. 2024

Phys. Chem. Chem. Phys.

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“…In recent years, several studies have focused on QSAR for AChE inhibitors [18][19][20][21]. These investigations have employed various statistical and machine learning methods, which have shown promising results in achieving good performance.…”

Section: Introductionmentioning

confidence: 99%

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

Noviandy,

Maulana,

Idroes

et al. 2023

Malacca Pharm.

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This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.

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

Cited by 13 publications

References 39 publications

A Review on Recent Approaches on Molecular Docking Studies of Novel Compounds Targeting Acetylcholinesterase in Alzheimer Disease

A Review on Recent Approaches on Molecular Docking Studies of Novel Compounds Targeting Acetylcholinesterase in Alzheimer Disease

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

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