Multi-source data integration for explainable miRNA-driven drug discovery

Explainable drug discovery driven by miRNA offers substantial application potential due to the high targetability of miRNAs. Despite their effectiveness, the current graph neural network (GNN)-based methods encounters three significant challenges. Initially, GNN-based prediction models, disseminating messages throughout the miRNA-disease graph, are susceptible to "over-smoothing". Secondly, these approaches frequently pinpoint potential miRNA drugs through established miRNA-disease associations (MDAs), neglecting the possible interdependence between unvalidated miRNAs and diseases. Thirdly, the lack of interpretability in these models, coupled with their dependence on specific dataset training, leads to suboptimal generalization capabilities. We present MS-EMD, an explainable model for miRNA-driven drug discovery that integrates subgraph estimation with an energy-constrained diffusion approach. This model aims to accurately forecast potential miRNA drugs by analyzing data from both local and global viewpoints. The MS-EMD model utilizes subgraph estimation to aggregate and update node representations using local subgraphs, thereby preventing over-smoothing. Simultaneously, it unveils potential miRNA-disease dependencies through a global attention mechanism driven by an energy-constrained diffusion process, improving model interpretability. Furthermore, we employ a data fusion technology that integrates diverse similarity data to strengthen the initial representations of miRNAs and diseases. Through various comparison and ablation studies, we verified the MS-EMD model's efficiency and stability, highlighting its potential as an explainable tool for miRNA-driven drug discovery. Our code and data are available at: https://github.com/lizhen5000/MS-EMD.git.