Targeting PRMT5 Across Cancers and AI-Driven Discovery of Candidate Compounds

[bioRxiv] Rana, Hossan, Rafi et al. published a preprint study combining pan-cancer multi-omics analysis with machine-learning virtual screening for targeting protein arginine methyltransferase 5 (PRMT5). The paper states that bioinformatics analysis showed PRMT5 has tumor-specific prognostic effects in GBM, LGG and KIRC; a deep neural network screened from approximately 1.5 million PubChem compounds, achieving a ROC-AUC of 0.9857, and preferentially selected three candidates: CID 44345182, CID 2794188 and CID 44345334. Following molecular docking, ADMET filtering, toxicity prediction and 100-nanosecond molecular dynamics simulations, CID 44345182 was identified as a potential hit, with a docking score of -9.8 kcal/mol, compared with -9.2 kcal/mol for the reference ligand. The authors state that experimental validation is still needed.