Discovering Protein Interactions and Repurposing Drugs in SARS-CoV-2 (COVID-19) via Learning on Robust Multipartite Graphs

The COVID-19 pandemic caused by SARS-CoV-2 has emphasized the importance of studying virus-host protein-protein interactions (PM) and drug -target interactions (DTIs) to discover effective antiviral drugs. While several computational algorithms have been developed for this purpose, most of them overlook the interplay pathways during infection along PPIs and DTIs. In this paper, we present a novel multipartite graph learning approach to uncover hidden binding affinities in PPIs and DTIs. Our method leverages a comprehensive biomolecular mechanism network that integrates protein-protein, genetic, and virus -host interactions, enabling us to learn a new graph that accurately captures the underlying connected components. Notably, our method identifies clustering structures directly from the new graph, eliminating the need for post-processing steps. To mitigate the detrimental effects of noisy or outlier data in sparse networks, we propose a robust objective function that incorporates the (sic)(2,p) -norm and a constraint based on the pth-order Ky-Fan norm applied to the graph Laplacian matrix. Additionally, we present an efficient optimization method tailored to our framework. Experimental results demonstrate the superiority of our approach over existing state-of-the-art techniques, as it successfully identities potential repurposable drugs for SARS-CoV-2, offering promising therapeutic options for COVID-19 treatment.