An　interaction　between　pharmacological　agents　can　trigger　unexpected　adverse　events.　Capturing　richer　and　more　comprehensive　information　about　drug-drug　interactions　(DDIs)　is　one　of　the　key　tasks　in　public　health　and　drug　development.　Recently,　several　knowledge　graph　(KG)　embedding　approaches　have　received　increasing　attention　in　the　DDI　domain　due　to　their　capability　of　projecting　drugs　and　interactions　into　a　low-dimensional　feature　space　for　predicting　links　and　classifying　triplets.　However,　existing　methods　only　apply　a　uniformly　random　mode　to　construct　negative　samples.　As　a　consequence,　these　samples　are　often　too　simplistic　to　train　an　effective　model.　In　this　paper,　we　propose　a　new　KG　embedding　framework　by　introducing　adversarial　autoencoders　(AAEs)　based　on　Wasserstein　distances　and　Gumbel-Softmax　relaxation　for　DDI　tasks.　In　our　framework,　the　autoencoder　is　employed　to　generate　high-quality　negative　samples　and　the　hidden　vector　of　the　autoencoder　is　regarded　as　a　plausible　drug　candidate.　Afterwards,　the　discriminator　learns　the　embeddings　of　drugs　and　interactions　based　on　both　positive　and　negative　triplets.　Meanwhile,　in　order　to　solve　vanishing　gradient　problems　on　the　discrete　representation-an　inherent　flaw　in　traditional　generative　models-we　utilize　the　Gumbel-Softmax　relaxation　and　the　Wasserstein　distance　to　train　the　embedding　model　steadily.　We　empirically　evaluate　our　method　on　two　tasks:　link　prediction　and　DDI　classification.　The　experimental　results　show　that　our　framework　can　attain　significant　improvements　and　noticeably　outperform　competitive　baselines.　Supplementary　information:　Supplementary　data　and　code　are　available　at　https://github.com/dyf0631/AAE_FOR_KG.