MDMD: A Computational Model for Predicting Drug-Related Microbes Based on the Aggregated Metapaths from a Heterogeneous Network

Clinical studies have shown that microbes in the human body are closely related to human health. Microbes can influence the activity and toxicity of drugs. So they play an important role in the treatment of diseases. It is critical to research the associations between drugs and microbes for drug development and precision medicine. Recently, there are several computation methods for predicting drug-related microbes. However, these methods ignore the information of diseases because diseases are the bridge between drugs and microbes. Here we introduce a new model (called MDMD) proposed to predict drug-related microbes based on the Metapaths from a heterogeneous network constructed by using the data of Diseases, Microbes, Drugs, the associations of microbe-disease and disease-drug. The MDMD uses an aggregation of the metapath features that can effectively abundance the embedding of the features for different types of nodes and edges in the heterogeneous networks. Then, the MDMD uses the attention mechanism to mark the importance of the metapath vector for each node type which can improve the quality of feature embedding. Experimental results demonstrate that the MDMD improves accuracy by 1.9% compared with other models. The MDMD is also used to predict the microbes of two drugs Lamivudine and Tenofovir which are the antiretroviral drugs used to treat the Acquired Immune Deficiency Syndrome(AIDS). The results show that 90-95% of microbes are reported in the PubMed. In addition, we found that lamivudine may be useful for the treatment of tuberculosis caused by Mycobacterium tuberculosis (Mtb). An online platform of the MDMD is available in https://mdmd2023.bit1024.top/, in which the source code of the MDMD and the data in the work can be downloaded.