Utilizing the knowledge graph for better drug discovery
Zhang Yu, Feng Sidu, Zhai Tianzhang, Zhu Jiuxu, Li Jian · 2024
Drug discovery affects human health and has also been one of the key research topics in the medical field. However, due to the long and complex process of drug development, it is difficult to develop a new drug. Fortunately, with the rise and development of artificial intelligence technology, many intelligent tools and algorithms have been developed to accelerate drug discovery. In this paper, we propose a method of drug discovery using knowledge graph. Firstly, we obtained data from a variety of public medical databases and constructed a knowledge graph of medical research and development containing a variety of omics data including drugs, genes, diseases, and the relationships between them. Then, in order to make better use of knowledge graphs, we use various knowledge graph embedding methods to convert the graph into a low-dimensional continuous embedding vector to capture the semantic association between entities. Finally, a case study exploring drug repurposing was conducted to demonstrate the effectiveness of knowledge graph-based link prediction for drug discovery. By integrating diverse biomedical data sources and combining different embeddings, we ensure that the semantic relationships between different entities in knowledge graphs can be fully understood by the model. Our method effectively takes advantage of the knowledge graphs, which will ultimately accelerate drug repurposing and lead to reduced costs.