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Mining drug-disease association and related interactions are essential for developing in silico drug repurposing (DR) methods and understanding underlying biological mechanisms. Recently, large-scale biological databases are increasingly available for pharmaceutical research, allowing for deep characterization for molecular informatics and drug discovery. However, DR is challenging due to the molecular heterogeneity of disease and diverse drug-disease associations. Importantly, the complexity of molecular target interactions, such as protein-protein interaction (PPI), remains to be elucidated. DR thus requires deep exploration of a multimodal biological network in an integrative context.

Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing