Evidence map›Paper›PMID 39906150›Full record

ArticleFrontiers in chemistry2025

Discovery of novel PRMT1 inhibitors: a combined approach using AI classification model and traditional virtual screening.

Jungan Zhang, Yixin Ren, Yun Teng, Han Wu, Jingsu Xue, Lulu Chen, Xiaoyue Song, Yan Li, Ying Zhou, Zongran Pang and 1 more

Abstract read
In one paragraph

Article in Frontiers in chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. PRMT5 is a prognostic-related biomarker associated with the tumor immune microenvironment in lung adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  4. Review
  5. Article
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

11 authors.

Jungan Zhang *School of Pharmacy, Minzu University of China, Beijing, China.
Yixin Ren *School of Pharmacy, Minzu University of China, Beijing, China.
Yun TengSchool of Pharmacy, Minzu University of China, Beijing, China.
Han WuSchool of Pharmacy, Minzu University of China, Beijing, China.
Jingsu XueInstitute of National Security, Minzu University of China, Beijing, China.
Lulu ChenInstitute of National Security, Minzu University of China, Beijing, China.
Xiaoyue SongSchool of Pharmacy, Minzu University of China, Beijing, China.
Yan LiSchool of Pharmacy, Minzu University of China, Beijing, China.
Ying ZhouSchool of Pharmacy, Minzu University of China, Beijing, China.
Zongran PangSchool of Pharmacy, Minzu University of China, Beijing, China.
Hao WangSchool of Pharmacy, Minzu University of China, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein arginine methyltransferases (PRMTs) play crucial roles in gene regulation, signal transduction, mRNA splicing, DNA repair, cell differentiation, and embryonic development. Due to its significant impact, PRMTs is a target for the prevention and treatment of various diseases. Among the PRMT family, PRMT1 is the most abundant and ubiquitously expressed in the human body. Although extensive research has been conducted on PRMT1, the reported inhibitors have not successfully passed clinical trials. In this study, deep learning was employed to analyze the characteristics of existing PRMTs inhibitors and to construct a classification model for PRMT1 inhibitors. Through a classification model and molecular docking, a series of potential PRMT1 inhibitors were identified. The representative compound (compound 156) demonstrates stable binding to the PRMT1 protein by molecular hybridization, molecular dynamics simulations, and binding free energy analyses. The study discovered novel scaffolds for potential PRMT1 inhibitors.

Indexed as

machine learningmolecular dockingmolecular dynamics simulationmolecular hybridizationPRMT1

Identifiers

PMID39906150
PMCPMC11788407

What Socratic 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 Socratic graph.