ArticleMolecular diversity2022
Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.
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 18 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
18 citing papers in PubMed, 44 citations in OpenAlex.
- BACE-1 inhibitors as potential drug candidates for treatment of Alzheimer's disease: a systematic review.Molecular diversity · 2026Review
- Article
- Computational prioritization of multi-target inhibitors: explainable QSAR and docking-based discovery of dual AChE/BACE1 chemotypes.Journal of computer-aided molecular design · 2026Article
- Perception of artificial intelligence and machine learning applications in the Nigerian healthcare sector: A cross-sectional study.PLOS global public health · 2026Article
- Multitarget-directed ligands in Alzheimer's disease: identification of AChE and BACE1 inhibitors by in silico approaches.Journal of computer-aided molecular design · 2025Review
- 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
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Revolutionizing Drug Delivery: The Impact of Advanced Materials Science and Technology on Precision Medicine.Pharmaceutics · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Computer-aided discovery of dual-target compounds for Alzheimer's from ayurvedic medicinal plants.PloS one · 2025Article
- Tribulations and future opportunities for artificial intelligence in precision medicine.Journal of translational medicine · 2024Review
- Screening Techniques for Drug Discovery in Alzheimer's Disease.ACS omega · 2024Review
- New Benzamides as Multi-Targeted Compounds: A Study on Synthesis, AChE and BACE1 Inhibitory Activity and Molecular Docking.International journal of molecular sciences · 2023Article
- The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies.Pharmaceuticals (Basel, Switzerland) · 2023Review
- 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
- Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and β-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R
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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.