A Microbe–Drug Association Prediction Model Based on Graph Attention Networks and Rotation Forest

Abstract

Background: In recent years, with the diversification and expansion of drug research in the medical field, the widespread use of drugs, particularly antibiotics, has led to increased microbial resistance. Consequently, exploring potential associations between drugs and microbes has become critically important. However, traditional biological experiments are extremely expensive and time-consuming. Therefore, developing more effective computational models for predicting potential associations between microbes and drugs is both essential and challenging. Results: We proposed GATROF, a hybrid heterogeneous graph-based framework for microbe–drug association prediction. In GATROF, by integrating multiple microbe–drug–disease similarity measures, we first constructed two distinct microbe–drug networks. In addition, based on different features of microbes and drugs, we further constructed two novel microbe–drug feature matrices. On this basis, the microbe–drug networks and the constructed feature matrices were further used in a Graph Attention Network to learn complementary topology-aware representations of microbes and drugs. These GAT-derived representations were then integrated with the constructed drug-side and microbe-side feature matrices and input into a Rotation Forest classifier for final association prediction. Experimental results and case studies demonstrated that GATROF predicts microbe–drug associations more accurately than existing state-of-the-art methods. Conclusion: GATROF provides a new integrated predictive framework for predicting potential microbe–drug associations. By combining heterogeneous biological information, GAT-based topological representation learning, and Rotation Forest classification, GATROF may help prioritize candidate drug–microbe associations for further biological validation.