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<p>Tumor heterogeneity and drug resistance limit single-agent therapies, making combination treatments essential. However, traditional screening methods are costly and inefficient. Here, we present DSimSynergy, a graph deep learning framework for predicting drug synergy. It constructs drug similarity networks from biological process and clinical applications, then learns drug representations through graph convolution on these networks. Subsequently, it combines them with graph attention representations of drug molecular fingerprints and cell line gene expressions to predict synergy scores for drug combinations. Comprehensive benchmarking on multiple independent datasets demonstrates that DSimSynergy consistently outperforms state-of-the-art methods. Model interpretability analysis revealed key genes and pathways underlying drug synergy, while validation on clinical patient and cohort data demonstrated good clinical translational potential and discovered the molecular mechanisms by which drugs generate synergistic effects through “pathway complementary networks”. DSimSynergy efficiently identifies synergistic combinations, reducing experimental costs while elucidating biological mechanisms to overcome resistance and guide personalized treatment.</p>

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Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Jiyin Lai,
  • Jiashuo Wu,
  • Yalan He,
  • Yongbao Zhang,
  • Bin Li,
  • Tingyu Shi,
  • Junwei Han

摘要

Tumor heterogeneity and drug resistance limit single-agent therapies, making combination treatments essential. However, traditional screening methods are costly and inefficient. Here, we present DSimSynergy, a graph deep learning framework for predicting drug synergy. It constructs drug similarity networks from biological process and clinical applications, then learns drug representations through graph convolution on these networks. Subsequently, it combines them with graph attention representations of drug molecular fingerprints and cell line gene expressions to predict synergy scores for drug combinations. Comprehensive benchmarking on multiple independent datasets demonstrates that DSimSynergy consistently outperforms state-of-the-art methods. Model interpretability analysis revealed key genes and pathways underlying drug synergy, while validation on clinical patient and cohort data demonstrated good clinical translational potential and discovered the molecular mechanisms by which drugs generate synergistic effects through “pathway complementary networks”. DSimSynergy efficiently identifies synergistic combinations, reducing experimental costs while elucidating biological mechanisms to overcome resistance and guide personalized treatment.