KGiA: Drug repurposing through disease-aware knowledge graph augmentation

Abstract

Objective:

Drug repurposing offers a cost-effective strategy to accelerate drug development by identifying new therapeutic uses for approved medications. Knowledge graphs (KGs) that capture large amounts of biomedical knowledge have recently been used for drug repurposing, however, KGs are inherently incomplete due to our limited biomedical knowledge.

Methods:

We propose KGiA, an inductive graph augmentation method that supports semi-inductive reasoning—allowing models to generalize to previously unseen biomedical entities. KGiA enhances KGs using counterfactual relationships mined from disease-specific topological patterns. We apply it to a state-of-art biomedical KG constructed from six datasets including biomedical relationships extracted from biomedical literature, which comprised 1,614,801 triples and 100,563 entities, including 30,006 diseases.

Results:

Across five augmented architectures, KGiA improves generalizability by up to 24× in Mean Reciprocal Rank (MRR) and outperforms the state-of-the-art KG-based drug repurposing model by up to 32%. We applied KGiA in four case studies of diseases including Alzheimer’s Disease and showed its promise in identifying novel repurposed candidate drugs.

Conclusion:

We showed that leveraging counterfactual relationships derived from disease-specific graph structures to augment existing knowledge graphs improved performance in KG-based drug repurposing.