MilGNet: A Multi-instance Learning-based Heterogeneous Graph Network for Drug repositioning

Abstract

The traditional wet-experiment-guided drug discovery is a labor-consuming and time-consuming process. Quite a few computational drug repositioning approaches have been proposed to predict potential drug-disease associations for the discovery of new indications for drugs and new therapies for diseases. Among them, heterogeneous graph neural network-based approaches can learn drug/disease topological representations on heterogeneous graphs and then give precise inferences for unconfirmed drug-disease associations. However, the existing approaches ignored the meta-paths in the drug-disease networks which could enhance the model performance and interpretability. In this study, we first proposed a multi-instance learning-based heterogeneous graph network approach for drug-disease association prediction, which is called MilGNet. Fusing with heterogeneous graph convolutional layer, the MilGNet learns meta-path-level representations for given drug-disease pairs by a novel pseudo meta-path instance generator and a bidirectional translating embedding projector. Then, an attention-based multi-scale interpretable joint predictor is assembled for precise and rational drug-disease association prediction. Comprehensive experiments have demonstrated the effectiveness of MilGNet compared to 6 advanced approaches. Meanwhile, the case study also shows the model interpretability of MilGNet by identifying high confident meta-paths. Our adopted benchmark dataset and source code are available at https://github.com/gu-yaowen/MilGNet..