Enhancing Drug-Drug Interaction Prediction with Context-Aware Architecture

In the field of disease treatment, the simultaneous use of multiple medications can lead to unforeseen adverse reactions, compromising patient safety and therapeutic efficacy. Consequently, predicting drug-drug interactions (DDIs) has emerged as a pivotal research focus to improve disease treatment. While recent advancements have been made in deep learning models for predicting drug pair relations, the nuanced consideration of individual or cellular conditions as influential contextual factors in DDIs is notably lacking. In this study, leveraging existing models, we introduce a methodology to predict DDIs through a context-aware architecture. The evident performance improvement compared to established methodologies underscores the crucial role of the context-aware mechanism in addressing context-conditional DDIs. Furthermore, we perform a systematic ablation analysis to assess the impact of model elements. Simultaneously, we also investigate the potential of incorporating pre-trained molecular representation learning models in this domain.