Relation-Aware Graph Transformer with Multi-View Consistency Alignment and Dynamic Fusion for Drug-Drug Interaction Prediction

Drug-Drug Interaction Prediction (DDI) is critical for ensuring safe medication. However, existing methods often rely on local structures or limited single-view molecular information, making them insufficient to capture long-range dependencies and multi-scale chemical semantics. To address these limitations, we propose MVCADF - a relation-aware Graph Transformer framework with multi-view consistency alignment and dynamic fusion for DDIs. Beyond leveraging molecular and biomedical knowledge graphs, MVCADF integrates motif fragment graphs, element-level view, and ECFP fingerprints, thus enabling comprehensive representations at the atom, fragment, element, fingerprint, and semantic levels. We design a multi-view consistency alignment strategy to project different views into a shared anchor space, which mitigates inter-view distributional shift and enhances representational coherence. This strategy is followed by a dynamic fusion module that adaptively integrates multi-view features based on their discriminative significance. Additionally, the relation- and distance-aware attention mechanism further facilitates modeling of long-range interactions within heterogeneous graphs. Extensive experiments show that MVCADF outperforms the best baseline by 3% - 5% across multiple public datasets, offering a robust and holistic solution for DDIs prediction.