AUTHOR=Zhang Jungan , Ren Yixin , Teng Yun , Wu Han , Xue Jingsu , Chen Lulu , Song Xiaoyue , Li Yan , Zhou Ying , Pang Zongran , Wang Hao 

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

JOURNAL=Frontiers in Chemistry

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1548812

DOI=10.3389/fchem.2025.1548812

ISSN=2296-2646

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.