Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment.

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

3 citing papers in PubMed.

1. AI-Driven discovery and experimental validation of covalent FGFR4 inhibitors for hepatocellular carcinoma.Journal of translational medicine · 2026
   Article
2. Rhaponitin Reverses Cisplatin Resistance and Impairs Cancer Stemness Through HIF-1α/MCT4/Wnt Pathway in Tongue Squamous Cell Carcinoma.The Kaohsiung journal of medical sciences · 2025
   Article
3. Discovery of compound 1105486 as a selective inhibitor of B4GALT1: potential for pancreatic cancer therapy.Frontiers in chemistry · 2025
   Article

Corrections and comments

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

Authors and funding

8 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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