Researchers unveiled KGLGANSynergy, a knowledge-graph and attention-network framework that predicts drug synergy with improved accuracy over prior models. The method integrates local and global attention mechanisms across biological networks to forecast which drug pairs will act synergistically in cancer and other disease models. The advance streamlines preclinical prioritization of combination therapies and could reduce experimental burden for pharma R&D teams. Biotech companies focusing on combination regimens may adopt such models for lead selection; the next step for developers is prospective validation in cell lines and animal models and integration with pharmacokinetic/toxicology constraints.