Reasoning over Relations: Modeling Inter-Relational Structure for Drug-Drug Interaction Prediction

Drug-drug interaction (DDI) prediction is essential for ensuring medication safety, and identifying the specific interaction type between a drug pair is of particular clinical value. Knowledge graphs (KGs) provide rich contextual information for this task. Existing methods typically model relations in KGs as fixed, type-specific representations, without encoding inter-relational structure. This limits the structural information available during entity representation learning. In this paper, we propose the Unconditioned Pair-anchored Relation Reasoning Network (UnPairRelNet), a multi-modal framework for DDI prediction. In particular, UnPairRelNet constructs a graph over relation types and generates relation representations that encode inter-relational structure, without dependence on any specific drug pair query. Subsequently, UnPairRelNet performs pair-conditioned bidirectional propagation anchored at the drug pair on the KG, with drug-pair attention aggregating local neighborhood information. The propagation reduces dependence on entity-specific parameters, naturally accommodating drugs unseen during training. In addition, UnPairRelNet encodes drug molecules at multiple granularities and fuses them with the KG representations for the final prediction. Extensive experiments on two benchmarks demonstrate that UnPairRelNet achieves the best performance on the majority of metrics and surpasses the strongest baseline by over 6% on macro F1 in scenarios involving novel drugs. The code is available at https://anonymous.4open.science/r/DDI1-03F6.