Prediction of Microbe-drug Associations Based on Chemical Structures and the KATZ Measure

Background: Microbial communities have important influences on our health and disease. Identifying potential human microbe-drug associations will be greatly advantageous to explore complex mechanisms of microbes in drug discovery, combinations and repositioning. Until now, the complex mechanism of microbe-drug associations remains unknown. Objective: Computational models play an important role in discovering hidden microbe-drug associations because biological experiments are time-consuming and expensive. Based on chemical structures of drugs and the KATZ measure, a new computational model (HMDAKATZ) is proposed for identifying potential Human Microbe-Drug Associations. Methods: In HMDAKATZ, the similarity between microbes is computed using the Gaussian Interaction Profile (GIP) kernel based on known human microbe-drug associations. The similarity between drugs is computed based on known human microbe-drug associations and chemical structures. Then, a microbe-drug heterogeneous network is constructed by integrating the microbe microbe network, the drug-drug network, and a known microbe-drug association network. Finally, we apply KATZ to identify potential associations between microbes and drugs. Results: The experimental results showed that HMDAKATZ achieved area under the curve (AUC) values of 0.9010 +/- 0.0020, 0.9066 +/- 0.0015, and 0.9116 in 5-fold cross-validation (5-fold CV), 10-fold cross-validation (10-fold CV), and leave one out cross-validation (LOOCV), respectively, which outperformed four other computational models(SNMF,RLS,HGBI, and NBI). Conclusion: HMDAKATZ obtained better prediction performance than four other methods in 5 fold CV, 10-fold CV, and LOOCV. Furthermore, three case studies also illustrated that HMDAKATZ is an effective way to discover hidden microbe-drug associations.

Automated summary

The study introduces a computational model, HMDAKATZ, for identifying potential human microbe-drug associations. Experimental results show that HMDAKATZ outperforms four other computational models in various cross-validation methods. Case studies also demonstrate that HMDAKATZ is an effective way to discover hidden microbe-drug associations.