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<p>Predicting cancer drug responses is crucial for precision medicine. This study proposes AMDRP, a novel model that predicts drug responses by integrating drug features—represented as molecular graphs and extended connectivity fingerprints (ECFP)—with multi-omics data from cancer cell lines. AMDRP incorporates an Adaptive Feature Fusion (AFF) module to dynamically weight and fuse these drug features, resulting in enhanced drug representations. Furthermore, a multi-head bidirectional cross-attention (MBCA) module is introduced to model deep interactions between drug and cell line features. Extensive experiments demonstrate that AMDRP achieves significantly higher prediction accuracy than state-of-the-art baselines. Ablation studies confirm the critical contribution of both modules, with ECFP features providing substantial performance gains. The model’s robustness and generalization capability were rigorously evaluated through cross-dataset validation and leave-one-out experiments, demonstrating its effectiveness against data distribution shifts. Predictions and enrichment analysis on unknown drug-cell line pairs underscore the model’s predictive power and biological relevance. These results indicate that AMDRP is an effective tool for predicting cancer drug responses and holds potential value for guiding anticancer drug discovery.</p>

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AMDRP: adaptive drug feature fusion and multihead bidirectional cross-attention network for drug-cancer cell response prediction

  • 期刊论文

  • 发表日期：

    2026年3月1日

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  • Shiqian Han,
  • Kaifeng Huang,
  • Jiahao Shi,
  • Jun Wang

摘要

Predicting cancer drug responses is crucial for precision medicine. This study proposes AMDRP, a novel model that predicts drug responses by integrating drug features—represented as molecular graphs and extended connectivity fingerprints (ECFP)—with multi-omics data from cancer cell lines. AMDRP incorporates an Adaptive Feature Fusion (AFF) module to dynamically weight and fuse these drug features, resulting in enhanced drug representations. Furthermore, a multi-head bidirectional cross-attention (MBCA) module is introduced to model deep interactions between drug and cell line features. Extensive experiments demonstrate that AMDRP achieves significantly higher prediction accuracy than state-of-the-art baselines. Ablation studies confirm the critical contribution of both modules, with ECFP features providing substantial performance gains. The model’s robustness and generalization capability were rigorously evaluated through cross-dataset validation and leave-one-out experiments, demonstrating its effectiveness against data distribution shifts. Predictions and enrichment analysis on unknown drug-cell line pairs underscore the model’s predictive power and biological relevance. These results indicate that AMDRP is an effective tool for predicting cancer drug responses and holds potential value for guiding anticancer drug discovery.