MCFusion-DDI: Multimodal Cross-Attention Fusion of Local-Global Features and Latent Drug Associations for Explainable DDI Prediction

Accurate prediction of drug-drug interactions (DDIs) is critical for ensuring patient safety in polypharmacy, yet remains challenging due to the complexity of the underlying biochemical mechanisms. Existing methods are limited by inadequate fusion of heterogeneous features, insufficient integration of local and global molecular characteristics, and lack of interpretability for novel drugs. These limitations stem from fragmented processing of structural and biological data, which overlooks hierarchical feature relationships and latent pharmacological associations. Here, we present MCFusion-DDI, a multimodal framework that unifies molecular substructures, chemical fingerprints, and drug similarity networks via three synergistic encoding channels. Our approach leverages a graph neural network with bond-level attention for hierarchical substructure extraction, a convolutional network for global fingerprint encoding, and similarity integration through network embeddings. The core innovation is a dual-channel cross-attention mechanism that dynamically integrates intra-drug feature dependencies and inter-drug interactions using efficient linearized attention combined with gated fusion. Predictions are generated using a Kolmogorov-Arnold network with adaptive activation functions. Extensive evaluations demonstrate state-of-the-art performance, with MCFusion-DDI achieving 8.2 - 12.7% improvements in AUROC and F1 score across the DrugBank, ZhangDDI, and ChCh-Miner datasets, exhibiting exceptional robustness in novel-drug scenarios. Attention visualizations provide mechanistic interpretability by highlighting high-risk substructures such as competitive hydroxyl groups in nonsteroidal anti-inflammatory drugs. This work establishes a new paradigm for explainable DDI prediction in precision medicine.