Researchers proposed a dual-pathway computational framework to predict rare and previously unseen drug–drug interactions using molecular graphs, biological knowledge networks, and semantic descriptions. The approach targets a persistent gap in interaction models—generalizing beyond known training pairs to combinations that have not yet been tested. For discovery pipelines, the key relevance is prioritization: better prediction can reduce the experimental burden in safety pharmacology and repositioning efforts. The framework’s design suggests an emphasis on interpretable biology plus representation learning. As drug–drug interaction libraries expand, computational methods that anticipate genuinely novel pairs are likely to become increasingly important for screening strategies.