  Interaction between pharmacological agents can trigger unexpected adverse
events. Capturing richer and more comprehensive information about drug-drug
interactions (DDI) is one of the key tasks in public health and drug
development. Recently, several knowledge graph 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 knowledge graph embedding framework by introducing adversarial
autoencoders (AAE) based on Wasserstein distances and Gumbel-Softmax relaxation
for drug-drug interactions 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.
