Code written for the paper submission to ICBDS 2024
The rising cost of drug discovery, coupled with a stagnation in the approval of novel treatments, highlights the urgent need for innovative strategies such as drug repurposing. Pharmaceutical companies invest roughly 10-15 years and $2.6 billion to get a single FDA-approved drug to market. The COVID-19 pandemic further underscored the necessity of quickly identifying existing drugs with potential efficacy against a fast-spreading virus to curtail the pandemic. In this study, we perform a comparative analysis of several Graph Neural Networks (GNNs) and recommendation system models to address drug repurposing. We construct an integrated graph that combines Protein-Protein Interaction networks, Drug-Target Protein graphs, Disease-Protein associations, and Drug-Disease links. We leverage a network learning paradigm implemented over this complex graph via both node-agnostic and heterogeneous graph techniques for link prediction in drug-disease pairs. We implement a Heterogeneous Graph Transformer (HGT) model that processes three node types (drugs, diseases, proteins) and four edge types. The HGT achieved an AUC-ROC of 0.985 and an F1-score of 0.90, demonstrating its efficacy in predicting drug repurposing candidates. Additionally, we compared several node-agnostic GNN architectures, including Graph Convolutional Networks, Graph Attention Networks, GraphSAGE, and Graph Isomorphism Networks. All architectures performed comparably, with an AUC-ROC of around 0.98. However, when framing the drug repurposing task as a recommendation problem using Matrix Factorization with side information, we observed a significant drop in performance, with the AUC-ROC falling to 0.82. This performance degradation highlights the importance of incorporating Protein-Protein Interaction networks in the modeling process, as matrix factorization fails to capture these complex network effects critical for drug repurposing. Our models ranked 6,158 drugs based on their predicted efficacy in treating COVID-19, providing a valuable tool for prioritizing clinical trials and further research. Beyond COVID-19, such an integrated framework can allow us to uncover drug-repurposing prospects for any other novel diseases in a significantly more efficient and cost-effective way.