Multi-relational knowledge graph for drug-drug interaction prediction via dual aggregation and collaborative optimization

Identifying potential drug-drug interactions is crucial in clinical care and new drug development, as mutual interference between drugs can lead to adverse reactions. Recently, computational methods have been widely employed for predicting DDIs. However, these approaches often encounter high computational complexity during the process of node feature representation and frequently overlook the interactivity and polysemy of entities. Effectively handling heterogeneous information remains a significant challenge. To cope with these limitations, we propose a dual aggregation and collaborative optimization learning framework，named MRACO，for DDIs prediction based on a multi-relational knowledge graph. MRACO utilizes structural information from these graphs, efficiently learning interactivity information across various relationship types. Additionally, MRACO employs dual aggregation operations to explicitly encode and aggregate multi-type information, capturing diverse semantic relationships of drug nodes. Moreover, MRACO simplifies complex computational processes by utilizing the collaborative loss optimization function, eliminating redundant information and enhancing model robustness. By integrating deep-level interactive information, MRACO leverages rich contextual data from knowledge graphs, enhancing prediction stability. Experiments demonstrate the effective of MRACO feature extraction capabilities in multi-relational networks, highlighting its understanding of the underlying mechanisms of DDIs.