An Efficient Drug-Drug Interactions Prediction Technology for Molecularly Intelligent Manufacturing

Drug-Drug Interactions (DDIs) prediction is an essential issue in the molecular ﬁeld. Traditional methods of observing DDIs in medical experiments require plenty of resources and labor. In this paper, we present a computational model dubbed MedKGQA based on Graph Neural Networks to auto-matically predict the DDIs after reading multiple medical documents in the form of multi-hop machine reading comprehension. We introduced a knowledge fusion system to obtain the complete nature of drugs and proteins and exploited a graph reasoning system to infer the drugs and proteins contained in the documents. Our model signiﬁcantly improves the performance compared to previous state-of-the-art models on the QA NGAROO M ED H OP dataset, which obtained a 4.5% improvement in terms of DDIs prediction accuracy.