GAALSMDA: A Graph Attention-based Fusion Network Integrating Dual Attention and BiLSTM for Microbe-Drug Association Prediction

Introduction: Microbes have increasingly become critical new drug targets in human health. However, the paucity of known microbe-drug association data hinders drug discovery. Predicting potential microbe-drug associations can complement traditional experiments and accelerate drug development, making it crucial to develop efficient computational methods. Methods: We proposed GAALSMDA, a graph attention-based fusion network. First, a microbe-drug heterogeneous network and feature matrix were constructed by integrating multiple similarities of microbes and drugs. Graph Attention Network (GAT) was used to mine low-dimensional features of microbes and drugs. Then, dual attention mechanism (CBAM) and Bidirectional Long Short-Term Memory (BiLSTM) were applied to fuse local and global features. Finally, a classifier output the likelihood scores of associations. Results: The experimental results indicated that the AUC and AUPR evaluation indices of the model reached 0.9900±0.0011, 0.9958±0.0015 and 0.9492±0.0051, 0.9668±0.0042 in MDAD and aBiofilm datasets, respectively, and the prediction performance was significantly superior to that of existing prediction methods. Discussion: The outstanding performance highlights GAALSMDA's ability to process sparse data and integrate multi-source information, addressing the limitations of previous models in terms of insufficient feature fusion. However, the similarity calculations of GIP and HIP may introduce parameter uncertainty, which still needs further optimization. Conclusion: Our model demonstrates effectiveness and reliability in accurately inferring potential microbe-drug associations.