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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • Lei Li,
  • Hongyu Zhang,
  • Chunhou Zheng,
  • Yansen Su

摘要

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.