In the last decades, people have been consuming and combining more drugsthan before, increasing the number of Drug-Drug Interactions (DDIs). To pre-dict unknown DDIs, recently, studies started incorporating Knowledge Graphs(KGs) since they are able to capture the relationships among entities provid-ing better drug representations than using a single drug property. In this paper,we propose an end-to-end framework that integrates several drug features frompublic drug repositories into a KG and embeds the nodes in the graph using var-ious translation, factorisation and Neural Network (NN) based KG Embedding(KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm thatpredicts unknown DDIs. Among the different translation and factorisation-basedKGE models, we found that the best performing combination was the ComplExembedding method with a Long Short-Term Memory (LSTM) network, whichobtained an F 1-score of 95.19% on a dataset based on the DDIs found in Drug-Bank version 5.1.8. This score is 5.61% better than the state-of-the-art modelDeepDDI. Additionally, we also developed a graph auto-encoder model that usesa Graph Neural Network (GNN), which achieved an F 1-score of 91.94%. Con-sequently, GNNs have demonstrated a stronger ability to mine the underlyingsemantics of the KG than the ComplEx model, and thus using higher dimensionembeddings within the GNN can lead to state-of-the-art performance.