融合知识图谱与深度学习的药物发现方法

The massive growing amount of biomedical literature brings huge challenges for data mining. In this paper, a method combining knowledge graph and deep learning is proposed to discover potential therapeutic drugs for disease of interest. Firstly, a biomedical knowledge graph is constructed with the relations extracted from biomedical literature. Then, the entities and relations of the knowledge graph are converted into low dimension continuous embeddings by knowledge graph embedding method. Finally, a recurrent neural network based drug discovery model is trained by using the known drug-disease related associations. The experimental results show that the proposed method can discover drugs for diseases and provide the drug mechanism of action.