ArticleChemical research in toxicology2023
Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.
Article in Chemical research in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 24 citations in OpenAlex.
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Diels-Alder Adducts fromInternational journal of molecular sciences · 2026Article
- AI-driven drug discovery using transformer-based molecular representation learning.Frontiers in artificial intelligence · 2026Article
- MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity.Artificial intelligence in the life sciences · 2025Article
- Big data and AI: Potential and challenges for digital transformation in toxicology.Environmental analysis, health and toxicology · 2025Article
- Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.Molecular diversity · 2025Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Adverse Outcome Pathway and Machine Learning to Predict Drug Induced Seizure Liability.ACS chemical neuroscience · 2025Article
- Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).Molecular pharmaceutics · 2025Article
- Mosquito mutations F290V and F331W expressed in acetylcholinesterase of the sand fly Phlebotomus papatasi (Scopoli): biochemical properties and inhibitor sensitivity.Parasites & vectors · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?Analytical and bioanalytical chemistry · 2025Review
- Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5.Molecular pharmacology · 2025Article
- Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis.Molecular diversity · 2024Article
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted 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.