ArticlePLoS computational biology2019
Using the drug-protein interactome to identify anti-ageing compounds for humans.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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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.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
- Network-driven discovery of repurposable drugs targeting hallmarks of aging.Nature aging · 2026Article
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- Structure-based machine learning screening identifies natural product candidates as potential geroprotectors.Journal of cheminformatics · 2025Article
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- Emerging roles of senolytics/senomorphics in HIV-related co-morbidities.Biochemical pharmacology · 2024Review
- Network pharmacology-based approach to investigate the molecular targets and molecular mechanisms of Rosmarinus officinalis L. for treating aging-related disorders.Biogerontology · 2024Review
- Pharmacology of Aging:Aging biology · 2024Article
- Network pharmacology, molecular docking, and molecular dynamics simulation to elucidate the mechanism of anti-aging action of Tinospora cordifolia.Molecular diversity · 2024Review
- Chaperone Activators.Sub-cellular biochemistry · 2024Review
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- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- We are all aging, and here's why.Aging medicine (Milton (N.S.W)) · 2022Review
- Hsp90: From Cellular to Organismal Proteostasis.Cells · 2022Review
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
- Repurposing new drug candidates and identifying crucial molecules underlying PCOS Pathogenesis Based On Bioinformatics Analysis.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2021Article
- Article
- Caenorhabditis elegans for rare disease modeling and drug discovery: strategies and strengths.Disease models & mechanisms · 2021Review
- The aging proteostasis decline: From nematode to human.Experimental cell research · 2021Review
- Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging.Aging cell · 2020Article
- SynergyAge, a curated database for synergistic and antagonistic interactions of longevity-associated genes.Scientific data · 2020Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Advancing age is the dominant risk factor for most of the major killer diseases in developed countries. Hence, ameliorating the effects of ageing may prevent multiple diseases simultaneously. Drugs licensed for human use against specific diseases have proved to be effective in extending lifespan and healthspan in animal models, suggesting that there is scope for drug repurposing in humans. New bioinformatic methods to identify and prioritise potential anti-ageing compounds for humans are therefore of interest. In this study, we first used drug-protein interaction information, to rank 1,147 drugs by their likelihood of targeting ageing-related gene products in humans. Among 19 statistically significant drugs, 6 have already been shown to have pro-longevity properties in animal models (p < 0.001). Using the targets of each drug, we established their association with ageing at multiple levels of biological action including pathways, functions and protein interactions. Finally, combining all the data, we calculated a ranked list of drugs that identified tanespimycin, an inhibitor of HSP-90, as the top-ranked novel anti-ageing candidate. We experimentally validated the pro-longevity effect of tanespimycin through its HSP-90 target in Caenorhabditis elegans.
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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.