DrugRep-HeSiaGraph: When heterogenous siamese neural network meets knowledge graphs for drug repurposing

Abstract

Abstract Background: The Drug repurposing is an approach that holds promise in identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues in constructing and embedding knowledge graphs. Results: This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-diseases knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-diseases knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.52%, a BS of 0.119, and an MCC of 69.12%. Conclusion: We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing.