ArticleNature aging2025
Discovering geroprotectors through the explainable artificial intelligence-based platform AgeXtend.
Article in Nature aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Insights into the therapeutic strategies for aging and aging-associated diseases.Signal transduction and targeted therapy · 2026Review
- Integrative analysis of plasma small-molecule and gut-microbiome markers of sarcopenia in a pilot study within an Indian cohort.Scientific reports · 2026Article
- In Silico Assessment of Potential Geroprotectors: From Separate Endpoints to Complex Pharmacotherapeutic Effects.International journal of molecular sciences · 2025Article
- ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan-Extending Compounds.Aging cell · 2025Article
- Structure-based machine learning screening identifies natural product candidates as potential geroprotectors.Journal of cheminformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Aging involves metabolic changes that lead to reduced cellular fitness, yet the role of many metabolites in aging is unclear. Understanding the mechanisms of known geroprotective molecules reveals insights into metabolic networks regulating aging and aids in identifying additional geroprotectors. Here we present AgeXtend, an artificial intelligence (AI)-based multimodal geroprotector prediction platform that leverages bioactivity data of known geroprotectors. AgeXtend encompasses modules that predict geroprotective potential, assess toxicity and identify target proteins and potential mechanisms. We found that AgeXtend accurately identified the pro-longevity effects of known geroprotectors excluded from training data, such as metformin and taurine. Using AgeXtend, we screened ~1.1 billion compounds and identified numerous potential geroprotectors, which we validated using yeast and Caenorhabditis elegans lifespan assays, as well as exploring microbiome-derived metabolites. Finally, we evaluated endogenous metabolites predicted as senomodulators using senescence assays in human fibroblasts, highlighting AgeXtend's potential to reveal unidentified geroprotectors and provide insights into aging mechanisms.
Indexed as
Identifiers
39627462What 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.