DREG： A large-scale biological knowledge graph for Drug Repurposing

Abstract

Background: Developing new drugs involves significant costs. However, repurposing existing drugs can identify potential application areas for treating new diseases. Current drug repurposing methods often require strong domain knowledge and numerous biological experiments. The knowledge graph approach which leverages existing drug research and development knowledge has emerged as a promising alternative. Results: This paper introduces the DREG (Drug Repurposing Entity Knowledge graph) model, which is based on a large-scale knowledge graph. By aggregating multiple related knowledge networks, the model extracts association information between known drug and disease entities. It then calculates entity similarity to predict unknown drug-disease relationships. The DREG model's performance, with an MRR index of 0.308 and Hits@10 of 0.628, surpasses that of the best-performing model by 4.7% (MRR) and 18.1% (Hits@10). The DREG model's effectiveness rate in quantitative experiments can reach 80%. The recommended results have clinical value, as some are already undergoing clinical trials on ClinicalTrials.gov and the China Clinical Trial Center. Conclusion: The DREG model can predict candidate drugs for repurposing, reducing the scope of human expert evaluation and treating new diseases.