ArticleACS omega2022
Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies.
Article in ACS omega, 2022. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- An Integrated Computational Workflow for Discovering Alkaloid-Derived Ligands of Cyclin-Dependent Kinase 2.Pharmaceuticals (Basel, Switzerland) · 2026Article
- TropMol-Caipora: A Cloud-Based Web Tool to Predict Cruzain Inhibitors by Machine Learning.ACS omega · 2026Article
- Phytochemicals from Astragalus zederbaueri as Acetylcholinesterase Inhibitors for Alzheimer's Therapy.PloS one · 2026Article
- Tripeptides inhibit dual targets AChE and BACE-1: a computational study.RSC advances · 2025Article
- Discovery of novel theophylline derivatives bearing tetrazole scaffold for the treatment of Alzheimer's disease.RSC advances · 2025Article
- Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.Royal Society open science · 2024Article
- Article
- Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors fromACS omega · 2024Article
- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
- Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs.Molecules (Basel, Switzerland) · 2023Review
- Probing the Reactions of Thiourea (CHACS omega · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC
Identifiers
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.