ArticleAging2017
Machine learning for predicting lifespan-extending chemical compounds.
Article in Aging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 53 citations in OpenAlex.
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Article
- 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
- Review
- Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.Journal of personalized medicine · 2024Review
- Autophagy and machine learning: Unanswered questions.Biochimica et biophysica acta. Molecular basis of disease · 2024Review
- Combinatorial interventions in aging.Nature aging · 2023Review
- Review
- Article
- Autophagy dark genes: Can we find them with machine learning?Natural sciences (Weinheim, Germany) · 2023Article
- Discovery of senolytics using machine learning.Nature communications · 2023Article
- Article
- Random forest classification for predicting lifespan-extending chemical compounds.Scientific reports · 2021Article
- Gonadotropin Releasing Hormone (Gnrh) Triggers Neurogenesis in the Hypothalamus of Adult Zebrafish.International journal of molecular sciences · 2021Article
- Theaflavin-regulated Imd condensates controliScience · 2021Article
- The quest to slow ageing through drug discovery.Nature reviews. Drug discovery · 2020Review
- Identifying Potential Ageing-Modulating Drugs In Silico.Trends in endocrinology and metabolism: TEM · 2019Review
- Computational tools for geroscience.Translational medicine of aging · 2019Article
- Using the drug-protein interactome to identify anti-ageing compounds for humans.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
Increasing age is a risk factor for many diseases; therefore developing pharmacological interventions that slow down ageing and consequently postpone the onset of many age-related diseases is highly desirable. In this work we analyse data from the DrugAge database, which contains chemical compounds and their effect on the lifespan of model organisms. Predictive models were built using the machine learning method random forests to predict whether or not a chemical compound will increase Caenorhabditis elegans' lifespan, using as features Gene Ontology (GO) terms annotated for proteins targeted by the compounds and chemical descriptors calculated from each compound's chemical structure. The model with the best predictive accuracy used both biological and chemical features, achieving a prediction accuracy of 80%. The top 20 most important GO terms include those related to mitochondrial processes, to enzymatic and immunological processes, and terms related to metabolic and transport processes. We applied our best model to predict compounds which are more likely to increase C. elegans' lifespan in the DGIdb database, where the effect of the compounds on an organism's lifespan is unknown. The top hit compounds can be broadly divided into four groups: compounds affecting mitochondria, compounds for cancer treatment, anti-inflammatories, and compounds for gonadotropin-releasing hormone therapies.
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.