A knowledge graph embedding-based method for predicting the synergistic effects of drug combinations

Predicting the synergistic effects of drug combinations can accelerate the identification process of novel potential combination therapies for clinical studies. Although extensive efforts have been made in the field, the problem is still challenging due to the high sparsity of drug combinations' synergy data and the existence of false positive combinations resulted from the noise in experiments. In this paper, we develop a Knowledge Graph Embedding-based method for predicting the synergistic effects of Drug Combinations, namely KGE-DC, which fully extracts the features of drug combinations. Firstly, a largescale knowledge graph including drugs, targets, enzymes and transporters is constructed, therefore, the sparsity of the drug combinations' data is reduced and the reliability of the data is increased. Then, knowledge graph embedding, which are capable of capturing complex semantic information of various entities in the knowledge graph, is adopted for learning low-dimensional representations for the drugs and cell lines. Finally, the synergy scores of drug combinations are predicted based on the drug and cell line embeddings of the drug combinations' synergy data. Extensive experiments on benchmark dataset with four different synergy types demonstrate that KGE-DC outperforms state-of the-art methods on both the regression and classification tasks, namely predicting the synergy scores of drug combinations and predicting whether the drug combinations are synergistic combinations. Our results indicate that KGE-DC is a valuable tool to facilitate the discovery of novel combination therapies for cancer treatment. The implemented code and experimental dataset are available online at https://github.com/yushenshashen/KGE-DC.