DSMV-DDI: A dual-level pharmacological semantic and stereochemical visual representation learning framework for...

Drug-drug interactions (DDIs) are a major cause of adverse drug events in clinical practice, especially under polypharmacy settings where patients receive multiple medications simultaneously. Reliable computational prediction of DDIs is therefore essential for improving medication safety and supporting clinical decision-making. Despite recent advances in computational DDI prediction, existing methods often struggle to jointly model multi-granularity pharmacological semantics and stereochemical molecular characteristics, limiting their ability to generalize to previously unseen drugs under cold-start scenarios. To address these limitations, we propose DSMV-DDI, a multimodal representation learning framework for drug-drug interaction prediction that integrates biomedical knowledge graph topology, chemical substructure features, dual-level pharmacological semantic representations, and stereochemical molecular visual representations derived from three-dimensional molecular conformations. In particular, the proposed dual-level semantic strategy jointly characterizes interaction-level pharmacological associations and intrinsic single-drug functional properties, enabling complementary modeling of pharmacological information across different semantic granularities. Furthermore, molecular visual representation learning captures geometric and spatial characteristics beyond topology-based molecular representations, improving generalization to topologically unseen drugs. Extensive experiments on real-world DDI datasets demonstrate that DSMV-DDI outperforms state-of-the-art methods, achieving an accuracy of 0.967 and an AUPR of 0.992 under the conventional setting. The proposed framework also maintains strong performance under both partial and complete cold-start settings. Ablation analyses show that dual-level pharmacological semantics contribute most to overall performance, while molecular visual representations provide complementary geometric information that further improves prediction accuracy.