Evidence map›Paper›PMID 41419749›Full record

ArticleScientific reports2025

AI-assisted identification of innovative phytochemicals from Aizoon canariense aimed at brachyury protein in chordoma: a computational strategy.

Shifaa O Alshammari

Abstract read
In one paragraph

Article in Scientific reports, 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

1 author.

Shifaa O AlshammariDepartment of Biology, College of Science, University of Hafr Al Batin, Hafr Al Batin, 31991, P.O. Box 1803, Saudi Arabia. dr.shifaa@uhb.edu.sa.

Funding

the Deanship of Research and Innovation at the University of Hafr Al Batin 0130-1446-S
6 · The paper itself

Abstract

Chordoma is an unique and aggressive bone malignancy along with limited therapeutic options, largely due to the undruggable nature of the TBXT oncoprotein. In this study, we employed an AI-assisted drug discovery approach to optimize β-sitosterol from Aizoon canariense against TBXT. Among the generated analogs, Artificial Intelligence Ligand 2 demonstrated strong inhibitory potential, showing a stable binding free energy of - 81.09 kcal/mol and maintaining conformational stability during a 500 ns molecular dynamics simulation. Key pharmacokinetic parameters revealed blood-brain barrier permeability, high gastrointestinal absorption as well as compliance with Lipinski's rules, while toxicity evaluation predicted moderate acute toxicity (LD₅₀ = 800 mg/kg, Class IV) with no major systemic risks. Density Functional Theory analysis confirmed molecular stability and reactivity suitable for biological interactions. Overall, these results highlight AI-Ligand 2 as a promising phytochemical derivative for targeting TBXT in chordoma, providing a foundation for future experimental validation and preclinical studies.

Indexed as

Artificial IntelligenceChordomaFetal ProteinsPhytochemicalsT-Box Domain ProteinsAnimalsBrachyury ProteinDrug DiscoveryHumansMolecular Dynamics SimulationSitosterolsBrachyury ProteinFetal Proteinsgamma-sitosterolPhytochemicalsSitosterolsT-Box Domain ProteinsArtificial intelligenceChordomaDensity functional theoryMolecular dynamics simulationTBXTΒ-sitosterol

Identifiers

PMID41419749
PMCPMC12717067

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.