Context-Aware Hierarchical Fusion for Drug Relational Learning.

The simultaneous use of multiple medications is a common practice in disease treatment, yet the same drug combination can lead to different effects under varying physiological, pharmacological, or genomic conditions-collectively referred to as the 'context'. Accurately predicting the outcomes of drug combinations across diverse contexts, also known as drug relational learning (DRL), is essential for improving therapeutic efficacy and safety. Despite its importance, existing methods face two major challenges: they are often tailored to specific DRL tasks, lacking generalizability, and they fail to explicitly model the influence of context on drug interactions. This limitation arises because most methods focus primarily on whole-drug compound structures, overlooking the fine-grained atomic-level interactions critical for context-aware predictions. To address these challenges, we propose a novel context-aware hierarchical fusion architecture for DRL. By formulating the problem as the label prediction of drug-drug-context triplets, our approach explicitly models the interaction between drugs by first learning their intrinsic atomic-level interactions and then incorporating context into their embeddings at the atomic level through information fusion. Experiments across diverse tasks-such as synergy prediction, polypharmacy side effect detection, and drug-drug interaction prediction-demonstrate our model's capability to effectively capture context-aware information. Importantly, our method consistently achieves robust performance in highly complex scenarios, highlighting its adaptability and utility in advancing context-aware drug relational learning.