Graph Network-Based Analysis of Disease-Gene-Drug Associations: Zero-Shot Disease-Drug Prediction and Analysis Strategies

Abstract

Existing drug repurposing methods have key limitations, primarily stemming from their reliance on known direct associations between diseases and drugs for supervised learning, as well as the need for large amounts of prior disease or drug information or feature data. In practice, many disease-drug connections remain unknown, and prior information is often complex and difficult to acquire and organize, limiting the applicability of these models. Furthermore, these models generally lack interpretability, making it difficult for experts to assess the reliability of predictions based solely on standard metrics, which raises doubts about the trustworthiness of their results. To address these challenges, we propose ZS-GNT, an innovative new workflow for zero-shot drug repurposing that leverages a novel and ingenious graph data meta-path linking scheme, which does not require any known disease-drug associations or their prior features. This approach is implemented using the Graph Neural Transformer (GNT) algorithm. The method infers disease-drug relationships indirectly through gene action, utilizing disease-gene associations and gene-drug interactions. It also generates a top drug-top gene linkage map, providing clinicians with a visual tool to assess the plausibility of suggested drugs before advancing to clinical trials. Experimental results show that, under the same linking scheme, the GNT algorithm achieved interaction link prediction accuracies of 95.86%, 99.28%, and 99.54% for three diseases, surpassing four other baseline methods. In a test involving a random selection of 100 diseases for drug discovery, among the top 5 recommended drugs from the candidates identified by ZS-GNT from a pool of 33,251 total drugs, the validation rate reached 47.05%, demonstrating the models effectiveness in drug discovery.