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<p>Drug–drug interactions (DDIs) can compromise therapeutic efficacy and patient safety, making accurate computational prediction highly important in drug discovery and clinical decision support. We propose Multi-GraphDDI, a structure-only framework that predicts DDIs without relying on external biological networks. In this model, three complementary molecular fingerprints, namely extended-connectivity fingerprints (ECFP4), PubChem fingerprints, and pharmacophore fingerprints, are encoded as three grayscale channels and fused into a single image representation, while a parallel branch transforms the two-dimensional molecular graph into a topology-aware embedding through a five-layer residual graph isomorphism network (GIN). A bidirectional feature-interaction module together with four-head cross-attention is then used to align the image-based and graph-based representations, and the fused features are further used to estimate interaction scores. On ChCh-Miner, ZhangDDI, and DeepDDI, Multi-GraphDDI achieved AUC/AUPR/F1 scores of 0.9986/0.9998/0.9730, 0.9858/0.9633/0.8855, and 0.9922/0.9920/0.9598, respectively, outperforming competing methods. These results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.</p>

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Multi-GraphDDI: Multi-Feature Fusion and Interaction for Graph-Based Drug-Drug Interaction Prediction

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Xiaodan Wang,
  • Hongjian Li,
  • Jihong Wang

摘要

Drug–drug interactions (DDIs) can compromise therapeutic efficacy and patient safety, making accurate computational prediction highly important in drug discovery and clinical decision support. We propose Multi-GraphDDI, a structure-only framework that predicts DDIs without relying on external biological networks. In this model, three complementary molecular fingerprints, namely extended-connectivity fingerprints (ECFP4), PubChem fingerprints, and pharmacophore fingerprints, are encoded as three grayscale channels and fused into a single image representation, while a parallel branch transforms the two-dimensional molecular graph into a topology-aware embedding through a five-layer residual graph isomorphism network (GIN). A bidirectional feature-interaction module together with four-head cross-attention is then used to align the image-based and graph-based representations, and the fused features are further used to estimate interaction scores. On ChCh-Miner, ZhangDDI, and DeepDDI, Multi-GraphDDI achieved AUC/AUPR/F1 scores of 0.9986/0.9998/0.9730, 0.9858/0.9633/0.8855, and 0.9922/0.9920/0.9598, respectively, outperforming competing methods. These results indicate that integrating heterogeneous structural cues through coarse- and fine-grained feature interaction provides an effective and scalable solution for DDI prediction.