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Background <p>Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein–protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy.</p> Results <p>We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein–protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneous molecular graphs. Extensive experiments show that MultiSyn outperforms several classical and state-of-the-art baselines in synergistic drug combination prediction tasks.</p> Conclusions <p>This study provides a powerful tool for inferring promising synergistic drug combinations. By leveraging attention mechanisms and pharmacophore information, MultiSyn identifies key substructures that are critical for synergy. Further visualization and case studies validate its effectiveness in capturing biologically meaningful features and identifying potential drug combinations.</p>

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Accurate prediction of synergistic drug combination using a multi-source information fusion framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Shuting Jin,
  • Huaze Long,
  • Anqi Huang,
  • Jianming Wang,
  • Xuan Yu,
  • Zhiwei Xu,
  • Junlin Xu

摘要

Background

Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alone. Nowadays, deep learning is widely applied in this field. However, most methods overlook the important role of protein–protein interaction networks formed by gene expression products and the pharmacophore information of drugs in predicting drug synergy.

Results

We propose MultiSyn, a multi-source information integration method for the accurate prediction of synergistic drug combinations. Specifically, we design a semi-supervised learning framework using an attributed graph neural network to integrate protein–protein interaction networks of gene expression products with multi-omics data, constructing initial cell line representations that incorporate multi-source information. Furthermore, we refine the initial cell line representation by adaptively integrating it with normalized gene expression profiles, enabling the extraction of cell line features that encapsulate global information. In addition, we decompose drugs into fragments containing pharmacophore information based on chemical reaction rules and construct a heterogeneous graph comprising atomic and fragment nodes. To enhance the capture of molecular structural information, we introduce a heterogeneous graph transformer to learn multi-view representations of heterogeneous molecular graphs. Extensive experiments show that MultiSyn outperforms several classical and state-of-the-art baselines in synergistic drug combination prediction tasks.

Conclusions

This study provides a powerful tool for inferring promising synergistic drug combinations. By leveraging attention mechanisms and pharmacophore information, MultiSyn identifies key substructures that are critical for synergy. Further visualization and case studies validate its effectiveness in capturing biologically meaningful features and identifying potential drug combinations.