HGBHAN: A Novel Framework for Microbe-Drug Interaction Prediction Using Heterogeneous Graphs and Bi-LSTM With Hierarchical Attention

Predicting microbe-drug associations (MDAs) is vital for accelerating drug discovery and optimizing clinical interventions in biomedical research. Traditional laboratory-based methods, though reliable, are constrained by high costs and limited scalability. While many computational approaches have utilized feature similarities to infer MDAs, they often overlook the complex and heterogeneous relationships inherent in biological networks, as well as the challenge posed by imbalanced datasets. In this study, we propose HGBHAN, a novel framework for MDAs prediction using heterogeneous graphs and bidirectional long short-term memory (Bi-LSTM) with hierarchical attention, for robust MDAs prediction. HGBHAN constructs a comprehensive heterogeneous network by integrating microbe and drug similarities with known association information, capturing multi-level structural and sequential dependencies. The model employs Bi-LSTM modules and a hierarchical attention mechanism to learn discriminative node embeddings, while residual connections are incorporated to address the over-smoothing issue in graph neural networks. Extensive experiments conducted on three public benchmark datasets demonstrate that HGBHAN outperforms existing models across multiple evaluation metrics, validating its efficacy in accurately predicting microbe-drug associations.