ArticleACS omega2020
Machine Learning for Discovery of GSK3β Inhibitors.
Article in ACS omega, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 44 citations in OpenAlex.
- Deep learning-assisted virtual screening of a large chemical library for selective GSK3β inhibitors.Molecular diversity · 2026Article
- Identification of a novel GSK-3β inhibitor for Alzheimer's disease using In-Silico prediction and experiment cycling, validated in a streptozotocin-induced Alzheimer's disease mouse model.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Computational discovery of ATP-competitive GSK3β inhibitors using database-driven virtual screening and deep learning.Molecular diversity · 2026Article
- Article
- Discovery of Small Molecule Inhibitors against Polo-Like Kinase 1 Targeting Breast Cancer.Anti-cancer agents in medicinal chemistry · 2026Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Fifteen years of ChEMBL and its role in cheminformatics and drug discovery.Journal of cheminformatics · 2025Review
- Discovery of Dual Targeting GSK-3β/HIV-1 Reverse Transcriptase Inhibitors as Neuroprotective Antiviral Agents.ACS chemical neuroscience · 2025Article
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification.Frontiers in radiology · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- Sertraline as a Multi-Target Modulator of AChE, COX-2, BACE-1, and GSK-3β: Computational and In Vivo Studies.Molecules (Basel, Switzerland) · 2024Article
- New Advances in the Pharmacology and Toxicology of Lithium: A Neurobiologically Oriented Overview.Pharmacological reviews · 2024Review
- Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation.Future medicinal chemistry · 2024Article
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
- Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.Frontiers in endocrinology · 2023Article
- The Commoditization of AI for Molecule Design.Artificial intelligence in the life sciences · 2022Article
- KUALA: a machine learning-driven framework for kinase inhibitors repositioning.Scientific reports · 2022Article
- MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction.ACS omega · 2022Article
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Alzheimer's disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC
Identifiers
What Socratic 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 Socratic graph.