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The complex processes of action of Chinese medicines and their formulations can be studied in terms of complex heterogeneous networks, however multiple diseases are different from one disease. In this study, we proposed a graph neural network-based modelling strategy to explore the material basis and mechanisms of the formulae under multiple disease conditions, exemplified by Xuefu Zhuyu Decoction for stable angina pectoris and unstable angina pectoris. Our modelling approach consists of two parts, a feature part and a model part. In the feature part, we calculated four similarities and their combinations in the similarity network as features of nodes of the heterogeneous network, using a variety of algorithms. In the model part, both the multi-label classification model for compounds and the link prediction model for compounds-targets were performed in six model comparison experiments and four partial ablation experiments. We confirmed that the GraphSAGE-based model was the best performing multi-label classification model while the GAT-based model was the best performing link prediction model, and that the full model with various similarity contents was optimal. Good generalization ability of the model was demonstrated through generalization ability experiments. In the case study, the model was used to predict potential compound-target interactions for marker targets and the reliability of the predictions was demonstrated by molecular docking and literature mining. In conclusion, the model successfully predicted the known material basis and mechanisms and also predicted unknown compound-marker target interactions. The proposed modelling approach can be extended to the study of other formulations.

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Application of a Complex Network Modeling Approach to Explore the Material Basis and Mechanisms of Traditional Chinese Medicine: A Case Study of Xuefu Zhuyu Decoction for the Treatment of Two Types of Angina Pectoris