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

Lianzhong Zhang1, Xiumin Shi2, Xiaohong Deng3

  • 1School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China.

Interdisciplinary Sciences, Computational Life Sciences
|October 28, 2025
PubMed
Summary

We developed MDL-HTI, a new computational framework for Traditional Chinese Medicine (TCM) that integrates graph learning and biological data to predict herb-target interactions (HTIs). This approach enhances understanding of TCM pharmacology and accelerates drug discovery.

Keywords:
Herb-target interaction predictionHeterogeneous graphMultimodal deep learningNetwork pharmacologyTraditional Chinese medicine

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Area of Science:

  • Computational pharmacology
  • Bioinformatics
  • Traditional Chinese Medicine (TCM) research

Background:

  • Traditional Chinese Medicine (TCM) offers unique therapeutic principles and vast medicinal resources, attracting global medical interest.
  • Understanding herb-target interactions (HTIs) is vital for elucidating TCM's pharmacological mechanisms and therapeutic effects.
  • Existing methods for identifying HTIs are limited and do not fully utilize available biological data.

Purpose of the Study:

  • To develop a novel computational framework, MDL-HTI, for more effective prediction of herb-target interactions (HTIs).
  • To integrate heterogeneous graph learning with multimodal biological data for enhanced HTI identification.
  • To overcome the limitations of current methods in leveraging comprehensive biological information for TCM research.

Main Methods:

  • Proposed MDL-HTI, a framework combining heterogeneous graph learning (using multi-view heterogeneous relation embedding - MV-HRE) and a biological multimodal information network.
  • MV-HRE extracts structural patterns from graphs, meta-paths, and communities.
  • A relational prediction network with self-attention dynamically fuses features for HTI identification.

Main Results:

  • MDL-HTI significantly outperformed state-of-the-art baseline methods in predicting HTIs.
  • Case study validation confirmed MDL-HTI's efficacy as a tool for identifying potential herb-target interactions.
  • The model demonstrates robust predictive capabilities for complex herb-target relationships.

Conclusions:

  • MDL-HTI establishes a novel computational paradigm for TCM pharmacology by integrating topological learning and multimodal biological data.
  • The framework offers a robust platform for elucidating TCM mechanisms and discovering multi-target herbs.
  • MDL-HTI has potential applications in precision medicine, reducing experimental costs and improving therapeutic outcomes.