Complex and diverse microbial communities do not only take important roles in human health and disease, but also are clinically drug targets. Predicting potential microbe-drug associations is helpful to understand complex mechanisms of microbes in clinical treatment, drug discovery, combinations, and repositioning. But potential microbe-drug association's prediction is time-consuming and expensive by using biological experiments, while computational methods can effectively overcome these limitations. To predict Human Microbe-Drug Association, a new computational method of KATZ measure (HMDAKATZ) is proposed. As we have known so far, HMDAKATZ is the first tool to predict potential associations between microbe and drug. In our method, we firstly construct the microbe similarity network by computing the GIP kernel similarity of microbes based on known microbe-drug associations. Then, the drug similarity network is constructed by integrating the chemical structures similarity and GIP kernel similarity of drugs. We further construct the microbe-drug heterogeneous network based on two similarity networks and known microbe-drug associations. Based on the microbe-drug heterogeneous network, we apply HMDAKATZ to predict potential microbe-drug associations based on the microbe-drug heterogeneous network. The experimental result shows that HMDAKATZ has obtained an average area under the curve (AUC) value of $0.9010\pm 0.0020$ in the 5-fold cross validation (5-fold CV). Furthermore, a case study also demonstrate that 100% of top 20 potential drugs of human immunodeficiency virus have been validated by existing literature, confirming the effectiveness of HMDAKATZ.