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

Abstract

Motivation: 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. Results: In this study, we propose BiFusion, a bipartite graph convolution network model for DR through heterogeneous information fusion. Our approach combines insights of multiscale pharmaceutical information by constructing a multirelational graph of drug-protein, disease-protein and PPIs. Especially, our model introduces protein nodes as a bridge for message passing among diverse biological domains, which provides insights into utilizing PPI for improved DR assessment. Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation. We offered an exploratory analysis for finding novel drug-disease associations. Extensive experiments showed that our approach achieved improved performance than multiple baselines for DR analysis.