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ORIGINAL RESEARCH article

Front. Chem.
Sec. Medicinal and Pharmaceutical Chemistry
Volume 13 - 2025 | doi: 10.3389/fchem.2025.1548812

Discovery of Novel PRMT1 Inhibitors: A Combined Approach Using AI Classification Model and Traditional Virtual Screening

Provisionally accepted
Jungan Zhang Jungan Zhang 1*Yixin Ren Yixin Ren 1,2Yun Teng Yun Teng 1*Han Wu Han Wu 1*Jingsu Xue Jingsu Xue 2*Lulu Chen Lulu Chen 2*Xiaoyue Song Xiaoyue Song 1*Yan Li Yan Li 1*Ying Zhou Ying Zhou 1*Zongran Pang Zongran Pang 1,3*Hao Wang Hao Wang 1,2,3*
  • 1 School of Pharmacy, Minzu University of China, Beijing, China
  • 2 Institute of National Security, Minzu University of China, Beijing, China
  • 3 Key Laboratory of Ethnomedicine (Minzu University of China), Beijing, China

The final, formatted version of the article will be published soon.

    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.

    Keywords: Prmt1, machine learning, molecular docking, Molecular Dynamics Simulation, Molecular hybridization

    Received: 20 Dec 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Zhang, Ren, Teng, Wu, Xue, Chen, Song, Li, Zhou, Pang and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jungan Zhang, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Yun Teng, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Han Wu, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Jingsu Xue, Institute of National Security, Minzu University of China, Beijing, 100081, China
    Lulu Chen, Institute of National Security, Minzu University of China, Beijing, 100081, China
    Xiaoyue Song, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Yan Li, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Ying Zhou, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Zongran Pang, School of Pharmacy, Minzu University of China, Beijing, 100081, China
    Hao Wang, School of Pharmacy, Minzu University of China, Beijing, 100081, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.