Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer. (PubMed, Comput Biol Med)
Using an advanced transformer-based deep learning approach, SELFormer, combined with QSAR analysis-based virtual screening, we identified previously unexplored FDA-approved drugs and natural compounds, i.e., Temsirolimus, Ergotamine, and capivasertib, with potential TRAILR1 modulatory effects. We propose TNFRSF10A as a therapeutically important PDAC vulnerability nurtured by spatially resolved expression patterns and dynamic molecular modeling. This study has used a novel integration of AI-implemented chemical modeling, high-throughput screening, and a multi-omics approach to unravel and pharmacologically target a cancer compartment-specific weakness in a notoriously drug-resistant cancer.