ArticleAlzheimer's research & therapy2022
Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease.
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
64 citing papers in PubMed, 2 syntheses or guidelines pooled it, 111 citations in OpenAlex.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- The landscape of the methodology in drug repurposing using human genomic data: a systematic review.Briefings in bioinformatics · 2024Pooled it
- The global research of artificial intelligence on Alzheimer disease: A 25-year bibliometric analysis.Medicine · 2026Article
- Leveraging Advanced AI Frameworks for Dual PPAR α/γ Agonist Discovery in Alzheimer's Disease.ACS chemical neuroscience · 2026Review
- Immune cell-specific genetic architecture of Alzheimer's disease revealed by multi-omics analysis for therapeutic target discovery and prioritization.Translational psychiatry · 2026Article
- A prototype-augmented graph representation learning framework for identifying brain disorder-associated genes and facilitating drug repurposing.PLoS computational biology · 2026Article
- From In Silico Hypothesis to Validation: The Role of Real-World Evidence in the Preliminary Verification of AI-Generated Drug-Repositioning Candidates: A Comprehensive Review.Journal of clinical medicine · 2026Review
- Single-cell analysis reveals neuroprotective histone deacetylase inhibitor pathways.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Single-Cell Transcriptomics Reveals Riluzole as an Osteoarthritis Candidate Drug via OB-NE Signaling Modulation and CTSS/NOS1 Inhibition.Drug design, development and therapy · 2026Article
- Alpinetin Nanoparticles Alleviate Optic Nerve Injury Induced by Acute Glaucoma via LRP1-PPARγ Mediated Regulation of Microglial Lipid Metabolism.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- A Knowledge Graph Approach To Discovering Drug Combination Therapies Across The Phenome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Machine learning guided virtual screening of FDA approved drugs targeting GSK-3β in Alzheimer's disease.Scientific reports · 2025Article
- Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.American journal of epidemiology · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Proinflammatory transcriptomic and kinomic alterations in astrocytes derived from patients with familial Alzheimer's disease.Brain, behavior, & immunity - health · 2025Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors at 8 institutions in 1 country.
Funding
Abstract
backgroundGenome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.
methodsTo address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.
resultsThrough this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.
conclusionsIn summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.
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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 Socratic graph.