# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
###### Abstract  
Predicting the side effects caused by drug combinations may facilitate the prescription of multiple medications in a clinical setting. So far, several prediction models of multidrug side effects based on knowledge graphs have been developed, showing good performance under constrained test conditions. However, these models usually focus on relationships between neighboring nodes of constituent drugs rather than whole nodes, and do not fully exploit the information about the occurrence of single drug side effects. The lack of learning the information on such relationships and single drug data may hinder improvement of performance. Moreover, compared with all possible drug combinations, the highly limited range of drug combinations used for model training prevents achieving high generalizability. To handle these problems, we propose a unified embedding-based prediction model using knowledge graph constructed with data of drug-protein and protein-protein interactions. Herein, single or multiple drugs or proteins are mapped into the same embedding space, allowing us to (1) jointly utilize side effect occurrence data associated with single drugs and multidrug combinations to train prediction models and (2) quantify connectivity strengths between drugs and other entities such as proteins. Due to these characteristics, it becomes also possible to utilize the quantified relationships between distant nodes, as well as neighboring nodes, of all possible multidrug combinations to regularize the models. Compared with existing methods, our model showed improved performance, especially in predicting the side effects of new combinations containing novel drugs that have no clinical information on polypharmacy effects. Furthermore, our unified embedding vectors have been shown to provide interpretability, albeit to a limited extent, for proteins highly associated with multidrug side effect. knowledge graph embedding: polypharmacy; multidrug side effect; neural networks; interpretability +
Footnote †: journal: Medical  
a drug, protein, pathway, cell, chemical substructure, or disease. Edges between nodes can represent different types of interactions between all biological entities represented by the nodes. In the previous studies employing knowledge graphs [8; 11; 13; 14; 15; 16], all types of drug-protein interactions are treated as a single edge type without subtype. Similarly, protein-protein interactions were treated as a single type without distinction between different subtypes. Despite such a simple graph structure, the models showed good performance in predicting multidrug side effect. Recently, several studies [10; 15] have employed more complex graph structures that distinguish node or edge subtypes. For example, [PERSON] et al. [10] used a graph that includes 11 node types (such as drugs, proteins, pathways, gene ontology, and disease) and 24 edge types. However, the effectiveness of complex graphs were not shown on predictive performance. Rather, it is possible that very complex graphs lead to performance degradation of the model due to learning many parameters defined for subtype. In order to make it easier manipulating knowledge graphs in real-world applications, many attempts have been made so far to embed the components of knowledge graphs into low-dimensional vector spaces [17; 18]. For example, some studies [10; 14; 16] have used graph convolution networks to refine a node's embedding vector by simply initializing the node embedding vector and repeatedly propagating it to neighboring nodes. On the other hand, in studies such as TransE [19], TransR [20], ComplEx [21], and DistMult [22], nodes were expressed as deterministic points in a vector space and the relationships between nodes were taken as operations in the vector space. Methods similar to these were applied in [PERSON] et al. [8] and [PERSON] et al. [11] for polypharmacy side effect prediction.