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To overcome limitations in existing compound representation methods, we propose CFC-CPI, a Cross-scale Feature Fusion approach for compound-protein interaction prediction. CFC-CPI integrates pharmacophore modeling with graph neural networks to represent compounds through atomic-scale (capturing atom types/bonds) and pharmacophore-scale (using BRICS-derived features) graph structures. This dual representation enables comprehensive characterization of compounds’ structural and functional properties. Experiments show CFC-CPI achieves state-of-the-art performance across multiple benchmark datasets, outperforming existing models on various metrics. Visualization analyses confirm the model’s ability to identify actual compound-protein binding sites, addressing interpretability concerns. In a SARS-CoV-2 virtual screening case study, CFC-CPI demonstrated exceptional performance, suggesting strong potential for drug repurposing applications.

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CFC-CPI: Cross-Scale Feature Fusion for Compound-Protein Interaction Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • Yanfei Li,
  • Qianxi Lin,
  • Zipeng Fan,
  • Peng Zhang

摘要

To overcome limitations in existing compound representation methods, we propose CFC-CPI, a Cross-scale Feature Fusion approach for compound-protein interaction prediction. CFC-CPI integrates pharmacophore modeling with graph neural networks to represent compounds through atomic-scale (capturing atom types/bonds) and pharmacophore-scale (using BRICS-derived features) graph structures. This dual representation enables comprehensive characterization of compounds’ structural and functional properties. Experiments show CFC-CPI achieves state-of-the-art performance across multiple benchmark datasets, outperforming existing models on various metrics. Visualization analyses confirm the model’s ability to identify actual compound-protein binding sites, addressing interpretability concerns. In a SARS-CoV-2 virtual screening case study, CFC-CPI demonstrated exceptional performance, suggesting strong potential for drug repurposing applications.