Adverse drug-drug interactions (DDIs) may occur when drugs are combined to treat complex or comorbid diseases, which can result in adverse drug events, injury, and even death. Therefore, accurate prediction of potential DDI events is critical. Recently, automated computational methods such as deep learning are widely used for DDI events prediction. However, most of these methods only consider single information about the drug or rely on a large amount of label data, which easily leads to insufficient robustness and generalization ability. Accordingly, we proposed a novel end-to-end graph contr...
