A review of deep learning approaches for drug synergy prediction in cancer

Synergistic drug combinations enhance cancer treatment by improving efficacy and reducing toxicity. With advances in artificial intelligence and large-scale datasets, deep learning has become central to anti-cancer drug synergy prediction. This review summarizes classical and emerging deep learning models from single-task learning and multi-task learning perspectives, discusses data and technical challenges, and highlights future directions for advancing computational drug synergy prediction.