MDL-HTI: A Multimodal Deep Learning Approach for Predicting Herb-Target Interactions

Purpose: Traditional Chinese medicine (TCM) has garnered increasing attention from the global medical community due to its unique therapeutic principles and extensive medicinal resources. Understanding herb-target interactions (HTIs) is crucial for elucidating the pharmacological mechanisms that link herbal medicines to biological targets, offering valuable insights into the precise effects of herbal therapeutics. However, current methods exhibit limited effectiveness and fail to fully leverage the biological information associated with herbs and targets. Methods: We propose MDL-HTI, a novel framework that integrates heterogeneous graph learning with multimodal biological data. The architecture employs a heterogeneous graph learning network based on the multi-view heterogeneous relation embedding (MV-HRE) algorithm to extract structural patterns from subgraphs, meta-paths, and communities, alongside a biological multimodal information network that encodes herbal ingredients, target pathways, and ligand properties into unified vectors. A relational prediction network with self-attention dynamically fuses features from both components to identify potential HTIs. Results: MDL-HTI demonstrates superior performance compared to state-of-the-art baselines. Furthermore, case study validation confirms that our model can serve as an effective tool for identifying potential HTIs. Conclusion: This work establishes a novel computational paradigm for TCM pharmacology by integrating topological learning with multimodal biological encoding. MDL-HTI provides a robust platform for elucidating TCM mechanisms and accelerating the discovery of multi-target herbs. The framework has potential applications in precision and personalized medicine, and its predictive capability may significantly reduce experimental costs while improving therapeutic outcomes for complex conditions.