DisenKGE-DDI: A Knowledge Graph Embedding Framework Based on Disentangled Graph Attention Networks for Drug-Drug...

Combination therapy is an essential strategy for treating complex diseases. However, unintended drug-drug interactions (DDIs) can compromise therapeutic efficacy or even cause severe adverse reactions, posing significant challenges to clinical safety and drug development. Accurate DDI prediction is therefore crucial for ensuring drug safety and guiding rational drug use. Although deep learning-based models have achieved significant progress in this field, most existing methods still face two major limitations: Some existing methods take the directionality of DDIs into account but overlook the diversity of interaction mechanisms, while others emphasize interaction diversity yet ignore directional effects. Such partial modeling fails to comprehensively capture pharmacological relationships and limits prediction accuracy. To overcome these challenges, we introduce DisenKGE-DDI, a novel framework based on a disentangled graph attention network, which enhances DDI prediction by incorporating both micro-disentanglement and macro-disentanglement mechanisms. At the micro-disentanglement level, a factor-aware relation-based message aggregation method is designed, leveraging a relation-aware guided routing strategy for selecting subsets of neighbors that are relevant to the current semantics and incorporating a dual-layer attention mechanism to learn embedding representations of different latent factors (components) of drug entities, precisely capturing intricate local semantic features. At the macro-disentanglement level, mutual information regularization is used to enforce independence among distinct semantic components, thereby ensuring that their representations remain non-interfering. This generates more adaptive drug embeddings that comprehensively capture the diverse interaction characteristics between drugs. Experimental results indicate that DisenKGE-DDI exhibits superior efficacy compared to state-of-the-art methods on public benchmark datasets, highlighting its superior performance in DDI prediction. Source code is available at https://github.com/HENU406/DisenKGE-DDI .