ArticleJournal of chemical information and modeling2023
Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
12 citing papers in PubMed, 17 citations in OpenAlex.
- Expanded Tox21 Biological Assay Panel for the Prediction of Drug-Induced Liver Injury and Cardiotoxicity.Environmental health perspectives · 2026Article
- Design, Synthesis, and Evaluation of Halogenated Indoles Amines as Acetylcholinesterase Inhibitors: Integrated In-Vitro and In-Silico Approaches.Chemistry & biodiversity · 2026Article
- A high-throughput screening platform for acetylcholinesterase inhibitors using a genetically encoded acetylcholine fluorescent sensor.Frontiers in bioengineering and biotechnology · 2026Article
- Design, Synthesis, and Evaluation of Pyrrole-Based Selective MAO-B Inhibitors with Additional AChE Inhibitory and Neuroprotective Properties Identified via Virtual Screening.Pharmaceuticals (Basel, Switzerland) · 2025Article
- The Selectivity of Butyrylcholinesterase Inhibitors Revisited.Molecules (Basel, Switzerland) · 2025Review
- Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.Communications chemistry · 2025Article
- Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening.Molecules (Basel, Switzerland) · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.Journal of chemical information and modeling · 2024Article
- Design, synthesis and preliminary biological evaluation of rivastigmine-INDY hybrids as multitarget ligands against Alzheimer's disease by targeting butyrylcholinesterase and DYRK1A/CLK1 kinases.RSC medicinal chemistry · 2024Article
- Identification of cholinesterases inhibitors from flavonoids derivatives for possible treatment of Alzheimer's disease:Current research in structural biology · 2024Article
- Use ofFrontiers in toxicology · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play important roles in human neurodegenerative disorders such as Alzheimer's disease. In this study, machine learning methods were applied to develop quantitative structure-activity relationship models for the prediction of novel AChE and BChE inhibitors based on data from quantitative high-throughput screening assays. The models were used to virtually screen an in-house collection of ∼360K compounds. The optimal models achieved good performance with area under the receiver operating characteristic curve values ranging from 0.83 ± 0.03 to 0.87 ± 0.01 for the prediction of AChE/BChE inhibition activity and selectivity. Experimental validation showed that the best-performing models increased the assay hit rate by several folds. We identified 88 novel AChE and 126 novel BChE inhibitors, 25% (AChE) and 53% (BChE) of which showed potent inhibitory effects (IC
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What OpenQuestion holds
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.