Study on Compatibility and Efficacy of Blood-activating Herb Pairs Based on Graph Convolution Network
10.13422/j.cnki.syfjx.20241817
- VernacularTitle:基于古今特征融合与图卷积网络的活血药对配伍预测
- Author:
Jingai WANG
1
;
Qikai NIU
1
;
Wenjing ZONG
1
;
Ziling ZENG
1
;
Siwei TIAN
1
;
Siqi ZHANG
1
;
Yuwen ZHAO
1
;
Huamin ZHANG
2
;
Bingjie HUO
3
;
Bing LI
1
Author Information
1. Institute of Chinese Materia Medica， China Academy of Chinese Medical Sciences， State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs， Institute of Chinese Materia Medica， China Academy of Chinese Medical Sciences， Beijing 100700， China
2. Institute of Basic Theory for Chinese Medicine， China Academy of Chinese Medicine Science， Beijing 100700， China
3. The Fourth Hospital of Hebei Medical University， Key Laboratory of Traditional Chinese Medicine Treatment of Digestive Tract Tumors in Hebei Province， Shijiazhuang 050010， China
- Publication Type:Journal Article
- Keywords:
Chinese medicine compatibility;
graph convolution network;
activating blood efficacy;
prediction model;
clinical decision-making
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2025;31(8):228-234
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveThis study aims to develop a prediction model for the compatibility of Chinese medicinal pairs based on Graph Convolutional Networks （GCN）， named HC-GCN. The model integrates the properties of herbs with modern pharmacological mechanisms to predict pairs with specific therapeutic effects. It serves as a demonstration by applying the model to predict and validate the efficacy of blood-activating herb pairs. MethodsThe training dataset for herb pair prediction was constructed by systematically collecting commonly used herb pairs along with their characteristic data， including Qi， flavor， meridian tropism， and target genes. Integrating traditional characteristics of herb with modern bioinformatics， we developed an efficacy-oriented herb pair compatibility prediction model （HC-GCN） using graph convolutional networks （GCN）. This model leverages machine learning to capture the complex relationships in herb pair compatibility， weighted by efficacy features. The performance of the HC-GCN model was evaluated using accuracy （ACC）， recall， precision， F1 score （F1）， and area under the ROC curve （AUC）. Its predictive effectiveness was then compared to five other machine learning models： eXtreme Gradient Boosting （XGBoost）， logistic regression （LR）， Naive Bayes， K-nearest neighbor （KNN）， and support vector machine （SVM）. ResultsUsing herb pairs with blood-activating effects as a demonstration， a prediction model was constructed based on a foundational dataset of 46 blood-activating herb pairs， incorporating their Qi， flavor， meridian tropism， and target gene characteristics. The HC-GCN model outperforms other commonly used machine learning models in key performance metrics， including ACC， recall， precision， F1 score， and AUC. Through the predictive analysis of the HC-GCN model， 60 herb pairs with blood-activating effects were successfully identified. Among of these potential herb pairs， 44 include at least one herb with blood-activating effects. ConclusionIn this study， we established an efficacy-oriented compatibility prediction model for herb pairs based on GCN by integrating the unique characteristics of traditional herbs with modern pharmacological mechanisms. This model demonstrated high predictive performance， offering a novel approach for the intelligent screening and optimization of traditional Chinese medicine prescriptions， as well as their clinical applications.
