Ensemble Based Method for Drug-Drug Interaction Prediction

Drug-Drug Interaction (DDI) is a major concern in medicine, as it can cause harmful effects and adverse side effects in patients. While existing computational approaches have progressed in DDI detection, they often fail to capture the complex semantic relationships and broader contextual patterns in biomedical literature. Current methods typically rely on either transformer-based models or traditional contextual embeddings but rarely leverage the complementary strengths of both approaches. This paper presents a novel hybrid architecture that combines the powerful contextual understanding of BioBERT with word embeddings trained on PubMed abstracts to enhance DDI detection accuracy.