# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
###### Abstract
Alternatively, random walks-based algorithms such as node2 vec [23] and edge2 vec [24] were developed to learn low-dimensional node representations in graphs. The node2 vec has been used for comparative purposes in several related studies for predicting multidrug side effects [7; 11; 14]. In recent years, hypergraphs in which hyperlinks can connect two or more nodes are being explored in order to represent complex higher-order relationships [25; 26]. Recent works on the use of hypergraphs for biological network analysis is presented in [27], but its potential has not yet been fully exploited for multidrug side effect prediction. Although several studies [7; 8; 9; 10; 11; 12; 13; 14; 15; 16] have successfully used knowledge graphs to predict multidrug side effects and improve model predictive performance or interpretability, they have not fully utilized some of the available information. For example, they mainly focused on learning the relationships between drug-drug characteristics and side effect occurrences into models. Therefore, the relationships between single drug characteristics and side effect occurrences were not considered important. In addition, only relationships between neighboring nodes around target drugs have been usually explored, while other relationships over the graphs were often ignored. If two nodes are not so close to each other, but there are numerous paths between them, they may have a strong relationship. Hence, it seems worthwhile to consider the relationships between drugs and all other biological entities (e.g., proteins) over the graphs, regardless of their closeness. In this study, we aimed to improve the generalizability of a multidrug side effect prediction model by embedding the components of the knowledge graph constructed from drug-protein interactions and protein-protein interactions into a unified vector space and using these embedding vectors for model training. We also further exploited the information on single drug adverse events and the relationships between drugs and all other nodes (i.e., proteins). Our main contributions are summarized as follows:  
* We introduce a unified embedding approach to convert all graph nodes (representing single or multiple drugs or proteins) into a single embedding space. Side effects caused by a single drug often contribute to multidrug side effects in polypharmacy. This single drug side effect information can help predict the side effects of various multidrug combinations. Herein, we proposed a unified embedding approach that enables the learning of single drug side effect information along with that of multidrug combinations. In this approach, all biological entities represented by nodes in the knowledge graph are mapped to their corresponding n-dimensional vectors that exist in the same embedding space. * We employ unified embedding vectors to leverage the side effect data related to single drugs as well as multidrug combinations. The embedding module in the proposed method generates the same n-dimensional vector for any given single or multidrug combination, regardless of the number of its constituent drugs. Thus, predictive model training is possible using data from single as well as multidrug combinations (labeled on the side effects that have occurred) represented by unified embedding vectors in the same latent space. * We utilize relationships between nodes (including distant nodes) for model regularization. Another feature of the proposed model is its ability to utilize the relationships between drugs and all other biological entities shown over the knowledge graph. In particular, for each single or multidrug combination, we computed the values of its connectivity strength to proteins, which indicate how closely its constituent drugs are related to each protein in the graph. We then used these connectivity strengths to regularize the model. * We use virtually created multidrug combinations for model regularization.