Evidence map›Paper›PMID 37175557›Full record

ArticleInternational journal of molecular sciences2023

AI-Predicted mTOR Inhibitor Reduces Cancer Cell Proliferation and Extends the Lifespan of

Tinka Vidovic, Alexander Dakhovnik, Oleksii Hrabovskyi, Michael R MacArthur, Collin Y Ewald

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
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

8 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
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  4. Translational Geroscience Strategies for Delaying Multimorbidity.ACS pharmacology & translational science · 2026
    Review
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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

5 authors at 3 institutions in 3 countries.

Tinka VidovicTinka Therapeutics, Fra Ivana Rozica 7, 21276 Vrgorac, Croatia.ORCID 0000-0003-0092-9365
Alexander DakhovnikLaboratory of Extracellular Matrix Regeneration, Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zürich, CH-8603 Schwerzenbach, Switzerland.
Oleksii HrabovskyiPalladin Institute of Biochemistry of the NAS of Ukraine, 02000 Kyiv, Ukraine.
Michael R MacArthurLewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA.
Collin Y EwaldLaboratory of Extracellular Matrix Regeneration, Institute of Translational Medicine, Department of Health Sciences and Technology, ETH Zürich, CH-8603 Schwerzenbach, Switzerland.ORCID 0000-0003-1166-4171
ETH Zurich · CHPalladin Institute of Biochemistry · UAPrinceton University · US

Funding

Swiss National Science Foundation 190072
6 · The paper itself

Abstract

The mechanistic target of rapamycin (mTOR) kinase is one of the top drug targets for promoting health and lifespan extension. Besides rapamycin, only a few other mTOR inhibitors have been developed and shown to be capable of slowing aging. We used machine learning to predict novel small molecules targeting mTOR. We selected one small molecule, TKA001, based on in silico predictions of a high on-target probability, low toxicity, favorable physicochemical properties, and preferable ADMET profile. We modeled TKA001 binding in silico by molecular docking and molecular dynamics. TKA001 potently inhibits both TOR complex 1 and 2 signaling in vitro. Furthermore, TKA001 inhibits human cancer cell proliferation in vitro and extends the lifespan of

Indexed as

Caenorhabditis elegans ProteinsNeoplasmsAnimalsArtificial IntelligenceCaenorhabditis elegansCell ProliferationHumansLongevityMolecular Docking SimulationMTOR InhibitorsSirolimusTOR Serine-Threonine KinasesCaenorhabditis elegans ProteinsMTOR InhibitorsMTOR protein, humanSirolimusTOR Serine-Threonine KinasesAI drug discoverycancerC. eleganslongevitymTORrapalog

Identifiers

PMID37175557
PMCPMC10177929
OpenAlexW4367052868

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.