ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Ethnopharmacology
This article is part of the Research TopicArtificial Intelligence in Traditional Medicine Research and ApplicationView all 18 articles
TCMRGAT: Relational Graph Attention Networks for Predicting Stroke Treatment Efficacy of Traditional Chinese Medicine Prescriptions
Provisionally accepted
- 1School of Informatics, Hunan University of Chinese Medicine, Changsha, Anhui Province, China
- 2Big Data Analysis Laboratory of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Anhui Province, China
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Background: Stroke is a serious neurological disorder that poses a global health challenge. Traditional Chinese Medicine (TCM) prescriptions have shown potential in its treatment. However, TCM prescriptions typically involve a wide variety of botanical drugs, and the efficacy of different combinations varies, with underlying patterns remaining unclear. This study aims to develop a model to predict the efficacy of TCM prescriptions for stroke, so as to deepen understanding of the underlying mechanisms of botanical drug therapies. Methods: We collected stroke-related TCM data, including prescriptions, botanical drugs, metabolites, and targets, from TCM classics and the HERB database. A generative adversarial network (GAN) was used to augment imbalanced data, and constructed a heterogeneous network. Then, we initialized node features and performed neighborhood feature learning using a relational graph attention network (RGAT) to predict TCM prescription efficacy. We compared our method, named RGAT for TCM prescription efficacy prediction (TCMRGAT), with other models. Results: TCMRGAT achieved an accuracy of 0.843 and an area under curve (AUC) of 0.853 on balanced data, outperforming competing methods. Ablation experiments confirmed the effectiveness of GAN-based data augmentation. Case studies using RGAT and GPT-4 highlighted the model's potential in real-world applications. Analysis of post-training attention weight changes revealed potential key botanical drug-metabolite relationships, suggesting they may be directly associated with stroke treatment. Conclusions: TCMRGAT aids in predicting prescription efficacy and identifying key metabolite s for stroke treatment. This study provides valuable insights into the use of Traditional Chinese Medicine for stroke and offers a promising direction for future research.
Keywords: Traditional Chinese Medicine, stroke treatment, Prescriptions prediction, Relational graph attention network, generative adversarial network, Prescriptions efficacy
Received: 02 Feb 2025; Accepted: 06 Nov 2025.
Copyright: © 2025 Cheng and Ding. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Changsong Ding, dingcs1975@hnucm.edu.cn
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