  Drug-drug interaction prediction is a crucial issue in molecular biology.
Traditional methods of observing drug-drug interactions through medical
experiments require significant resources and labor. This paper presents a
medical knowledge graph question answering model, dubbed MedKGQA, that predicts
drug-drug interaction by employing machine reading comprehension from
closed-domain literature and constructing a knowledge graph of drug-protein
triplets from open-domain documents. The model vectorizes the drug-protein
target attributes in the graph using entity embeddings and establishes directed
connections between drug and protein entities based on the metabolic
interaction pathways of protein targets in the human body. This aligns multiple
external knowledge and applies it to learn the graph neural network. Without
bells and whistles, the proposed model achieved a 4.5% improvement in terms of
drug-drug interaction prediction accuracy compared to previous state-of-the-art
models on the Qangaroo MedHop dataset. Experimental results demonstrate the
efficiency and effectiveness of the model and verify the feasibility of
integrating external knowledge in machine reading comprehension tasks.
