Exploring Microbe-Drug Association Prediction via Multi-Attribute Dual-Decoder Graph Autoencoder

Predicting potential microbe-drug associations (MDA) can help study pathogenesis, expedite pharmaceutical innovation, and enhance targeted therapeutics. Given the time and labor intensity of traditional biological experiments, an increasing number of computational approaches are being employed to predict MDA. The method based on graph embedding is one of the most widely used. However, most of these methods only consider node embedding or graph structure information in isolation, which leads to restricted predictive accuracy. In this work, we propose a method called exploring microbe-drug association prediction via multi-attribute dual-decoder graph autoencoder (MDGAEMDA). Specifically, a heterogeneous network containing microbe similarity, drug similarity, and known associations is constructed. Second, to enrich the node information, the multi-attribute features are obtained by importing the topological information of microbe and drug. Then, two heterogeneous networks constructed by the graph masking strategy are input into dual-decoder graph autoencoder that contains one encoder and two decoders (node decoder and structure decoder) to learn both node embedding and graph structure information. Finally, two low-dimensional features are spliced into the features of MDA pairs and predicted by random forest. The model was compared with multiple advanced methods using public datasets. The experimental outcomes showed that our model significantly outperformed other methods. The case study of widely used drugs demonstrated the reliability of the proposed method to predict MDA.