Researchers published KGLGANSynergy, a knowledge‑graph and attention‑based framework that improves prediction of drug synergy across compound pairs. The model integrates local and global attention mechanisms with structured biomedical knowledge graphs to prioritize synergistic combinations more accurately than prior computational approaches. The method could accelerate combination therapy discovery by filtering preclinical and clinical candidates, lowering screening costs and focusing experimental resources. Knowledge graphs encode entities and relationships (drugs, targets, pathways); attention networks weight the most relevant connections. Pharmaceutical discovery teams should assess KGLGANSynergy for integration into combo‑screen pipelines and retrospective validation against trial outcomes.