Structural Network Embedding using Multi-modal Deep Auto-encoders for Predicting Drug-drug Interactions

Predicting drug-drug interactions (DDIs) is crucial for patient safety and public health. The existing DDI prediction methods mainly fall into three categories: knowledge-based, similarity-based and network-based. Most recently, studies have demonstrated that integrating heterogeneous drug features is significantly important for developing high-accuracy prediction models, but it also brings many new challenges, i.e. heterogeneous properties, non-linear relations and incomplete data. In this paper, we propose a multi-modal deep auto-encoders based drug representation learning method for the DDI prediction, abbreviated as DDI-MDAE. The proposed method learns unified representations of drugs simultaneously from multiple drug feature networks using multi-modal deep auto-encoders. Then we adopt several operators on the learned drug embeddings to represent drug-drug pairs, and utilize the random forest to train models for the DDI prediction. Experimental results show that DDI-MDAE effectively learns the representations of drugs by fusing diverse information, and outperforms the other state-of-the-art benchmark methods. More importantly, DDI-MDAE works even for drugs without any known interaction.