KGMAEDDI: Knowledge Graph and Molecular-Graph Masked Autoencoder for Drug-Drug Interaction Prediction

Drug-drug interaction (DDI) prediction is essential for drug development and clinical safety. Early studies mainly relied on large labeled datasets and focused on structural or sequential drug features, often overlooking topological relationships with biomedical entities such as genes, diseases, and pathways. Although recent approaches have leveraged knowledge graphs (KGs), they typically neglect molecular structural information. To address these limitations, we propose KGMAEDDI, a novel framework that integrates molecular structures and semantic knowledge from KGs for DDI prediction. Specifically, KGMAEDDI employs a message-passing neural network to capture intrinsic structural features of drugs and a knowledge-aware attention network to extract semantic-rich representations from KGs. These representations are fused via a reconstruction-driven feature fusion module that combines a masked autoencoder and bi-directional cross-attention. This design enforces mutual reconstruction between modalities, thereby aligning structural and semantic embeddings in a shared latent space. We evaluate KGMAEDDI on the DrugBank dataset under both binary and multi-class settings. Experimental results show that KGMAEDDI consistently outperforms state-of-the-art baselines, validating its effectiveness in modeling complex drug interactions.