ArticleNature communications2023
Discovery of senolytics using machine learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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
73 citing papers in PubMed, 1 synthesis or guideline pooled it, 109 citations in OpenAlex.
- Rejuvenation of Bone Marrow Mesenchymal Stem Cells: Mechanisms and Their Application in Senile Osteoporosis Treatment.Biomolecules · 2025Pooled it
- Traditional Chinese Medicine Formulas in Delaying Aging: From Theoretical Foundations to Molecular Mechanisms and Translational Perspectives.Journal of cellular and molecular medicine · 2026Review
- A compendium of circulating biomarkers of senescence in humans: Insights on mechanistic impact across health domains and modulation by therapeutic interventions.Ageing research reviews · 2026Review
- Robots and Minimal, Physics-Informed Features: A Hybrid Framework for Enzyme Catalysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Closing the gap in aging science: unlocking the potential of nanoparticles in senescence therapy.Biogerontology · 2026Review
- Skin Aging: From Molecular Mechanisms to Therapeutic and Technological Innovations.Journal of cellular and molecular medicine · 2026Review
- Natural products targeting cytokine-regulated SASP inflammation networks in Ovarian cancer: Implications on immune escape and molecular resistance.Inflammopharmacology · 2026Review
- Article
- Sustainable Discovery of Natural Anti-Aging Bioactives from Food Resources: Current Status and Machine Learning Perspectives.Current issues in molecular biology · 2026Review
- Fate Bifurcation of Cellular Senescence: Dynamic Regulation from Tumor Suppression to Recurrence Risk.Cells · 2026Review
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Insights into the therapeutic strategies for aging and aging-associated diseases.Signal transduction and targeted therapy · 2026Review
- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- Review
- Chemical anchoring of immunotherapeutic drugs within senescent tumor cells overcomes senescence-driven immunotherapy resistance.Nature communications · 2026Article
- Translational Geroscience Strategies for Delaying Multimorbidity.ACS pharmacology & translational science · 2026Review
- Emerging strategies in senotherapeutics: from broad-spectrum senolysis to precision reprogramming.npj aging · 2026Review
- Discovery of TDP-43 aggregation inhibitorsbioRxiv : the preprint server for biology · 2026Article
- Deep learning-based discovery of tetrahydrocarbazoles as broad-spectrum antitumor agents and click-activated strategy for targeted cancer therapy.Acta pharmaceutica Sinica. B · 2026Article
- Chimeric Antigen Receptor T Cells as Living Therapeutics Targeting Senescence and Age-Related Diseases.Research (Washington, D.C.) · 2026Review
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.
Indexed as
Identifiers
What 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.