Journal of ethnopharmacology · 2026
MAGED: Multimodal attentive graph learning with gene expression dynamics on knowledge graphs for TCM target prediction.
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Chen Fengming, Fang Shichao, Zhao Ranran, Han Xingxing, Li Huan, Xu Bing, Tang Zhishu
Аннотация
ETHNOPHARMACOLOGICAL RELEVANCE: Plants used in traditional medicine have long provided valuable clues for drug discovery, yet systematically connecting their documented therapeutic effects with modern understanding of biological mechanisms remains a key scientific question. Bridging the gap between traditional knowledge, clinical symptom profiles, and contemporary molecular-level regulation is essential for advancing the study of herbal medicine.
OBJECTIVES: This study aims to develop a multimodal graph attention learning framework, named MAGED, which integrates knowledge graphs with gene expression dynamics to improve the accuracy and interpretability of Herb-Target Interaction (HTI) prediction.
METHODS: MAGED incorporates multi-source heterogeneous data, including traditional Chinese medicine (TCM) properties, hierarchical clinical symptom information, and molecular-level biological data, such as gene expression dynamics under drug intervention and causal biological networks. A multimodal fusion encoder is employed to embed herb attributes into efficacy-related feature vectors, which are then combined with functional representations (e.g., normalized enrichment scores) to form a unified contextualized representation. Additionally, a hierarchical graph attention network is designed to integrate macroscopic symptom-gene associations with microscopic regulatory pathways, thereby establishing a coherent inference chain from herbal effects to biological targets and phenotypic symptoms.
RESULTS: Systematic experimental evaluations demonstrated that MAGED significantly outperforms existing baseline methods across multiple evaluation metrics, achieving a 59.8 % improvement in HR@10. The model also exhibited superior ranking and recall performance under cold-start scenarios. In a case study on Scutellaria baicalensis, 8 out of the top 10 predicted targets were supported by existing literature, with functional or direct interaction evidence, and some targets were further validated through molecular docking.
CONCLUSION: MAGED provides an accurate and interpretable framework for predicting herb-target interactions, effectively integrating traditional knowledge with modern molecular evidence. This approach shows strong potential to facilitate the discovery of herbal mechanisms and the identification of novel therapeutic targets.
OBJECTIVES: This study aims to develop a multimodal graph attention learning framework, named MAGED, which integrates knowledge graphs with gene expression dynamics to improve the accuracy and interpretability of Herb-Target Interaction (HTI) prediction.
METHODS: MAGED incorporates multi-source heterogeneous data, including traditional Chinese medicine (TCM) properties, hierarchical clinical symptom information, and molecular-level biological data, such as gene expression dynamics under drug intervention and causal biological networks. A multimodal fusion encoder is employed to embed herb attributes into efficacy-related feature vectors, which are then combined with functional representations (e.g., normalized enrichment scores) to form a unified contextualized representation. Additionally, a hierarchical graph attention network is designed to integrate macroscopic symptom-gene associations with microscopic regulatory pathways, thereby establishing a coherent inference chain from herbal effects to biological targets and phenotypic symptoms.
RESULTS: Systematic experimental evaluations demonstrated that MAGED significantly outperforms existing baseline methods across multiple evaluation metrics, achieving a 59.8 % improvement in HR@10. The model also exhibited superior ranking and recall performance under cold-start scenarios. In a case study on Scutellaria baicalensis, 8 out of the top 10 predicted targets were supported by existing literature, with functional or direct interaction evidence, and some targets were further validated through molecular docking.
CONCLUSION: MAGED provides an accurate and interpretable framework for predicting herb-target interactions, effectively integrating traditional knowledge with modern molecular evidence. This approach shows strong potential to facilitate the discovery of herbal mechanisms and the identification of novel therapeutic targets.