MVGF-DR: Multi-view Graph Feature Fusion Approach for Drug Repositioning.

Abstract

Drug repositioning, exploring new indications for existing drugs, is emerging as a promising approach to accelerate drug discovery and reduce research risk of failure. Recent advances in this topic by applying graph neural networks have enabled researches to achieve significant results by extracting latent features from the original data. However, the previous studies have not fully considered the distinctive information embedded within different construction graphs, which may lead to insufficient classification performance due to the lack of more detailed features. This work therefore proposes a novel approach, namely MVGF DR, which leverages graph network construction and multi view graph feature fusion for drug repositioning. MVGF-DR built a comprehensive graph network from both similarity and association information, i.e., a similarity graph network is constructed with drug-drug and disease-disease similarities where similarity information are extracted by graph isomorphism networks, and an association graph network with drug-disease associations where drug-disease relationships are explored by graph convolutional networks. Additionally, a maximum value selection strategy is introduced to filter features from different channels for feature fusion and noise reduction. The average AUROC and AUPR achieved by MVGF-DR across the three datasets reached 95.38% and 51.20%, respectively, outperforming the other five state-of-the-art models. Multiple experiments further also demonstrated the flexibility and practical applicability of MVGF-DR.