From Data to Treatment: AI-Driven Identification of Molecular Targets and Bio-Inspired Therapeutics for Pediatric Glioma

Pediatric gliomas, including high-risk tumors like diffuse midline glioma (DMG/DIPG) and diffuse leptomeningeal glioneuronal tumor (DLGNT), remain some of the most challenging childhood cancers to treat. Despite advances in research, effective therapies are still limited, and survival rates for aggressive forms remain poor.
Our study aims to leverage artificial intelligence (AI) and multi-omics analysis to discover new molecular targets and bio-inspired therapeutics for pediatric gliomas. By analyzing genomic, transcriptomic, and clinical data from patients, our AI models will identify patterns that could reveal novel treatment strategies. We will then screen a database of nearly 2 million natural and bio-inspired compounds to identify promising drug candidates for potential future therapies.