Target Identification

"AI-driven target identification harnesses large amounts of biological data to determine key vulnerabilities in cancer. A recent study applied AI-powered software to transcriptomic data from adenoid cystic carcinoma (ACC)—a rare type of salivary gland cancer with few treatment options—and identified the protein PRMT5, an enzyme that modifies other proteins to control gene expression, as a promising therapeutic target. They showed that inhibition of PRMT5 suppressed tumor growth in multiple preclinical models and reduced the expression of key drivers of ACC progression, providing a strong rationale for future clinical development of PRMT5 inhibitors as a targeted therapy for ACC patients (1049)Mishra V, et al. (2025) J Exp Clin Cancer Res, 44: 11.. In another study, researchers developed a DL model that integrates multi-omic data and information on known pathways from public databases to predict cancer-driving genes. By modeling interactions between genes, proteins, and RNAs, the tool revealed 57 cancer-driving gene candidates across multiple tumor types, including three genes that had not been identified by other models (1050)Su X, et al. (2025) Nat Biomed Eng, 9: 371.. These findings highlight the potential of AI to uncover novel, clinically relevant targets, paving the way for the development of more effective and personalized therapies."