Medical knowledge graph question answering for drug-drug interaction prediction based on multi-hop machine reading comprehension

Abstract

Drug-drug interaction (DDI) prediction is a crucial issue in molecular biology. Traditional methods of observing drug-drug interactions through medical experiments require significant resources and labour. The authors present a Medical Knowledge Graph Question Answering (MedKGQA) model, dubbed MedKGQA, that predicts DDI by employing machine reading comprehension (MRC) from closed-domain literature and constructing a knowledge graph of “drug-protein” triplets from open-domain documents. The model vectorises 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 DDI 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 MRC tasks.