Evidence map›Paper›PMID 41332661›Full record

ArticlebioRxiv : the preprint server for biology2025

Autonomous AI Agents Discover Aging Interventions from Millions of Molecular Profiles.

Kejun Ying, Alexander Tyshkovskiy, Alibek Moldakozhayev, Hanchen Wang, Cecília G De Magalhães, Sharif Iqbal, Amanda E Garza, Albina Tskhay, Jesse R Poganik, Kexin Huang and 21 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors.

Kejun YingDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-1791-6176
Alexander TyshkovskiyDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-6215-190X
Alibek MoldakozhayevDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Hanchen WangDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Cecília G De MagalhãesDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Sharif IqbalDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Amanda E GarzaDivision of Endocrinology, Diabetes and Hypertension, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Albina TskhayDepartment of Family Medicine, McGill University, Montreal, QC, Canada.
Jesse R PoganikDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Kexin HuangDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Yuanhao QuDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Dmitrii GlubokovDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Cheng JinAvinasi Labs Limited, Sea Meadow House, P.O. Box 116, British Virgin Islands.
Donghyun LeeDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Hanna LiuDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Carolina LeoteResearch and Early Development, Genentech, South San Francisco, CA, USA.
Alexandre TrappDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-7938-0410
Lucas Paulo de Lima CamilloSchool of Clinical Medicine, University of Cambridge, UK.
Csaba KerepesiDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-9541-246X
Mahdi MoqriDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-6675-0566
Odin ZhangInstitute for Protein Design, University of Washington, Seattle, WA, USA.
Kaiyi JiangOmenn-Darling Bioengineering Institute, Princeton University, Princeton, NJ, USA.
Fedor GalkinInsilico Medicine, Boston, MA, USA.
Alex ZhavoronkovInsilico Medicine, Boston, MA, USA.
Jeremy M Van RaamsdonkDepartment of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
Mengdi WangPrinceton University, Department of Electrical and Computer Engineering & Center for Statistics and Machine Learning, Princeton, NJ, USA.
Le CongStanford University School of Medicine, Stanford, CA, USA.
Aviv RegevResearch and Early Development, Genentech, South San Francisco, CA, USA.
Jure LeskovecDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Tony Wyss-CorayStanford University School of Medicine, Stanford, CA, USA.
Vadim N GladyshevDivision of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-0372-7016

Funding

Using causal aging biomarkers and protein design to develop novel anti-aging interventionsK00AG088431 · NIA · STANFORD UNIVERSITY · PI Kejun Ying · 2025 to 2026
$176k
Using causal aging biomarkers and protein design to develop novel anti-aging interventionsF99AG088431 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI YING, KEJUN · 2024 to 2024
$37k
NIA NIH HHS F99 AG088431NIA NIH HHS K00 AG088431
6 · The paper itself

Abstract

Decades of publicly available molecular studies have generated millions of samples testing diverse interventions, yet these datasets were rarely analyzed for their effects on aging. Aging clocks now enable biological age estimation and life outcome prediction from molecular data, creating an opportunity to systematically mine this untapped resource. We developed ClockBase Agent, a publicly accessible platform that reanalyzes millions of human and mouse methylation and RNA-seq samples by integrating them with over 40 aging clock predictions. ClockBase Agent employs specialized AI agents that autonomously generate aging-focused hypotheses, evaluate intervention effects on biological age, conduct literature reviews, and produce scientific reports across all datasets. Reanalyzing 43,602 intervention-control comparisons through multiple aging biomarkers revealed thousands of age-modifying effects missed by original investigators, including over 500 interventions that significantly reduce biological age (e.g., ouabain, KMO inhibitor, fenofibrate, and NF1 knockout). Large-scale systematic analysis reveals fundamental patterns: significantly more interventions accelerate rather than decelerate aging, disease states predominantly accelerate biological age, and loss-of-function genetic approaches systematically outperform gain-of-function strategies in decelerating aging. As validation, we show that identified interventions converge on canonical longevity pathways and with strong concordance to independent lifespan databases. We further experimentally validated ouabain, a top-scoring AI-identified candidate, demonstrating reduced frailty progression, decreased neuroinflammation, and improved cardiac function in aged mice. ClockBase Agent establishes a paradigm where specialized AI agents systematically reanalyze all prior research to identify age-modifying interventions autonomously, transforming how we extract biological insights from existing data to advance human healthspan and longevity.

Identifiers

PMID41332661
PMCPMC12667862

What OpenQuestion holds

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Registered trials

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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.