Predicting Adverse Drug-Drug Interactions via Semi-supervised Variational Autoencoders.

Adverse Drug-Drug Interactions (DDIs) are a very important risk factor in the medical process, which may lead to readmission or death. Although a part of DDIs can be obtained through in vitro or in vivo experiments in the drug development stage, a large number of new DDIs still appear after the market, more and more researchers begin to pay attention to the research related to drug molecules, such as drug discovery, drug target prediction, DDIs prediction, etc. In recent years, many computational methods for predicting DDIs have been proposed. However, most of them only used labeled data and neglect a lot of information hidden in unlabeled data. Moreover, they always focus on binary prediction instead of multiclass prediction, although the exact DDI type is very helpful for our reasonable choice of medication. In this paper, a Semi-Surpervised Variational Autoencoders (SPRAT) method for predicting DDIs is proposed, which is composed of a neural network classifier and a Variational autoencoders (VAE). Classifier is the core components, VAE plays a role of calibration. In the end, the predicted label is a multi-hot vector which indicates specific DDI types between drug pairs. Finally, the experiments on real world dataset demonstrate the effectiveness of the proposed method in this paper.