ArticlePeerJ2016
Probing the origins of human acetylcholinesterase inhibition via QSAR modeling and molecular docking.
Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 74 citations in OpenAlex.
- Recent Advances in Pyrazole-Based Cholinesterase Inhibitors: Medicinal Chemistry Perspectives from 2020 to 2025.Pharmaceuticals (Basel, Switzerland) · 2026Review
- The Pharmacokinetic Properties of Veterinary Antiparasitic Drugs in the Context of Human Pregnancy-An In Silico Study.International journal of molecular sciences · 2026Article
- Identifying antivirals against influenza PA endonuclease with machine learning-based activity prediction, DFT optimization, and molecular dynamics simulation.Molecular diversity · 2026Article
- PPGBioPred: a webserver for predicting the bioactivity of compounds against PPARγ involved in the negative regulation of the Wnt/β-catenin signaling pathway.Molecular diversity · 2026Article
- Integrating multi-structure covalent docking with machine-learning consensus scoring enhances potency ranking of human acetylcholinesterase inhibitors.Briefings in bioinformatics · 2026Article
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- Drug repurposing targeting COVID-19 3CL protease using molecular docking and machine learning regression approaches.Scientific reports · 2025Article
- In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19.PloS one · 2025Article
- Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer-Design, Synthesis, and In Vitro Evaluation.ACS omega · 2023Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
- Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning.Molecules (Basel, Switzerland) · 2023Article
- Searching glycolate oxidase inhibitors based on QSAR, molecular docking, and molecular dynamic simulation approaches.Scientific reports · 2022Article
- Hybrid Approach to Identifying Druglikeness Leading Compounds against COVID-19 3CL Protease.Pharmaceuticals (Basel, Switzerland) · 2022Article
- In silico study of tacrine and acetylcholine binding profile with human acetylcholinesterase: docking and electronic structure.Journal of molecular modeling · 2022Article
- ABCpred: a webserver for the discovery of acetyl- and butyryl-cholinesterase inhibitors.Molecular diversity · 2022Article
- Alkaloid Profiling and Cholinesterase Inhibitory Potential ofPlants (Basel, Switzerland) · 2021Article
- Synthesis of some novel coumarin isoxazol sulfonamide hybrid compounds, 3D-QSAR studies, and antibacterial evaluation.Scientific reports · 2021Article
- The Role of Organic Small Molecules in Pain Management.Molecules (Basel, Switzerland) · 2021Review
- Construction of Quantitative Structure Activity Relationship (QSAR) Models to Predict Potency of Structurally Diversed Janus Kinase 2 Inhibitors.Molecules (Basel, Switzerland) · 2019Article
- TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease which leads to the gradual loss of neuronal cells. Several hypotheses for AD exists (e.g., cholinergic, amyloid, tau hypotheses, etc.). As per the cholinergic hypothesis, the deficiency of choline is responsible for AD; therefore, the inhibition of AChE is a lucrative therapeutic strategy for the treatment of AD. Acetylcholinesterase (AChE) is an enzyme that catalyzes the breakdown of the neurotransmitter acetylcholine that is essential for cognition and memory. A large non-redundant data set of 2,570 compounds with reported IC50 values against AChE was obtained from ChEMBL and employed in quantitative structure-activity relationship (QSAR) study so as to gain insights on their origin of bioactivity. AChE inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 different data splits using random forest. Generated models afforded R (2), [Formula: see text] and [Formula: see text] values in ranges of 0.66-0.93, 0.55-0.79 and 0.56-0.81 for the training set, 10-fold cross-validated set and external set, respectively. The best model built using the substructure count was selected according to the OECD guidelines and it afforded R (2), [Formula: see text] and [Formula: see text] values of 0.92 ± 0.01, 0.78 ± 0.06 and 0.78 ± 0.05, respectively. Furthermore, Y-scrambling was applied to evaluate the possibility of chance correlation of the predictive model. Subsequently, a thorough analysis of the substructure fingerprint count was conducted to provide informative insights on the inhibitory activity of AChE inhibitors. Moreover, Kennard-Stone sampling of the actives were applied to select 30 diverse compounds for further molecular docking studies in order to gain structural insights on the origin of AChE inhibition. Site-moiety mapping of compounds from the diversity set revealed three binding anchors encompassing both hydrogen bonding and van der Waals interaction. Molecular docking revealed that compounds 13, 5 and 28 exhibited the lowest binding energies of -12.2, -12.0 and -12.0 kcal/mol, respectively, against human AChE, which is modulated by hydrogen bonding, π-π stacking and hydrophobic interaction inside the binding pocket. These information may be used as guidelines for the design of novel and robust AChE inhibitors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.