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It is crucial to identify DDIs and explore their underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need for experimental search over a large space of drug combinations. Thus, many computational methods have been developed to predict DDIs, most of them focusing on whether a drug interacts with another or not. And a few deep learning-based methods address a more realistic screening task for identifying various DDI types, but they assume a DDI only triggers one pharmacological effect, while a DDI can trigger more types of pharmacological effects. Thus, here we proposed a novel end-to-end deep learning-based method (called deepMDDI) for the Multi-label prediction of Drug-Drug Interactions. deepMDDI contains an encoder derived from relational graph convolutional networks and a tensor-like decoder to uniformly model interactions. deepMDDI is not only efficient for DDI transductive prediction, but also inductive prediction. The experimental results show that our model is superior to other state-of-the-art deep learning-based methods. We also validated the power of deepMDDI in the DDIs multi-label prediction and found several new valid DDIs in the case study. In conclusion, deepMDDI is beneficial to uncover the mechanism and regularity of DDIs. • Introducing the deep relational graph convolutional network to design an encoder for capturing the topological features of DDI network. • Employing a tensor-like decoder to uniformly model both single-fold interactions and multi-fold interactions. • DeepMDDI can effectively discriminate whether an unlabeled type-specific drug pair results in one or more pharmacological effects. • DeepMDDI can not only predict the DDI in the network, but also predict the potential DDIs between the known drugs in the network and new drugs outside the network. [ABSTRACT FROM AUTHOR]