TCMRGAT: Relational graph attention networks for predicting stroke treatment efficacy of traditional Chinese medicine prescriptions

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. Conclusion 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.