ArticleMolecular diversity2022
Machine learning-based modeling to predict inhibitors of acetylcholinesterase.
Article in Molecular diversity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
19 citing papers in PubMed.
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.Molecular diversity · 2025Article
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- Prediction of Anti-rheumatoid Arthritis Natural Products of Xanthocerais Lignum Based on LC-MS and Artificial Intelligence.Combinatorial chemistry & high throughput screening · 2025Article
- Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.Drug design, development and therapy · 2025Article
- AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
- FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.Molecular diversity · 2024Article
- TNFMolecular diversity · 2024Article
- Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors fromACS omega · 2024Article
- Valorization and Repurposing of Citrus limetta Fruit Waste for Fabrication of Multifunctional AgNPs and Their Diverse Nanomedicinal Applications.Applied biochemistry and biotechnology · 2024Article
- Machine learning and molecular docking prediction of potential inhibitors against dengue virus.Frontiers in chemistry · 2024Article
- Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- Machine Learning-Based Virtual Screening and Molecular Simulation Approaches Identified Novel Potential Inhibitors for Cancer Therapy.Biomedicines · 2023Article
- Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease.Molecules (Basel, Switzerland) · 2023Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
- Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation.Frontiers in molecular biosciences · 2023Article
- Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.Molecular diversity · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use, which leads to enhanced memory and cognitive ability. The aim of the present work is to create machine learning models for distinguishing between AChE inhibitors and non-inhibitors using algorithms like support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The developed models were evaluated by 10-fold cross-validation and external dataset. Descriptor analysis was performed to identify most important features for the activity of molecules. Descriptors which were identified as important include maxssCH2, minHssNH, SaasC, minssCH2, bit 128 MACCS key, bit 104 MACCS key, bit 24 estate fingerprint and bit 18 estate fingerprints. The model developed using fingerprints based on random forest algorithm produced better results compared to other models. The overall accuracy of best model on test set was 85.38 percent. The developed model is available at http://14.139.57.41/achepredictor/ .
Indexed as
Identifiers
33891263What 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.