Biomedical Knowledge Graph Embedding with Capsule Network for Multi-label Drug-Drug Interaction Prediction

Abstract

Drug-drug interaction (DDI) plays an important role in drug development and administration. Most of existing network-based computation models regard the DDI prediction as a binary classification problem and generate negative DDI samples randomly, but the binary classification is not in line with the real problem since there are dozens of types of DDI and randomly generating negative samples may introduce false-negative samples since the non-observed facts can be either false or just missing. To address the above limitations, we propose a new framework called KG2ECapsule that explicitly models the multi-relational DDI data based on biomedical knowledge graphs in an end-to-end fashion. It first generates high-quality negative samples based on the average number of tail entities and head entities for each relation to reduce false-negative samples to some extent. KG2ECapsule then refines the representations of entities by recursively propagating the embeddings from the attention-based receptive fields of entities. Empirical results on three biomedical knowledge graphs of different scales show that KG2ECapsule outperforms the state-of-the-art methods consistently in multi-label DDI prediction task and further studies verify the efficacy of both probability-based sampling strategy and non-linear transformation for modeling multi-relational data.