摘要
目的:探究中药成分寒热药性预测模型的构建及应用。方法:基于中药信息数据库获取646味中药,涵盖10053个化合物成分。构建中药化合物分子指纹与分子图表征编码,分别构建图卷积神经网络(GCN)、K-近邻(KNN)、决策树(DT)、随机森林(RF)、支持向量机(SVM)算法,训练模型并调整模型超参数。将数据集划分为训练集与测试集,使用准确率、精确率、召回率与F值评价模型预测性能。将预测的高概率化合物分析中药寒热属性的生物学机制潜在差异。通过细胞增殖-毒性测试实验检测梯度加权得分的寒性中药成分对大鼠肾上腺髓质嗜铬瘤(PC-12)细胞氧糖剥夺再灌注(OGD/R)模型的保护作用。结果:GCN模型在中药寒热属性预测任务中综合表现良好。通过GCN模型从代表性寒热药物中筛选出的高概率寒热分类化合物共形成413靶点,与17通路具有潜在关联。细胞实验结果,随寒性加权分数降低逐渐显现为对OGD/R的细胞保护作用。结论:在寒热属性预测任务中,基于分子图表征的GCN模型相对于分子指纹表征的传统机器学习模型具有更优的性能,可为进一步探究中药“性-构”关系提供算法支持。
Objective:To explore the building and application of the prediction model of cold/hot properties of Chinese herbal medicines.Methods:A total of 646 Chinese herbal medicines,involving 10053 compounds,were obtained from the traditional Chinese medicine information database.The molecular fingerprints and molecular graph representation coding of Chinese medicinal compounds were established,and the graph convolutional neural network(GCN),K nearest neighbor(KNN),decision tree(DT),random forest(RF),and support vector machine(SVM)algorithms were constructed,respectively.The hyperparameters of each model were adjusted.The data set was divided into a training set and a test set,and the accuracy,precision,recall,and F value of each model were determined to evaluate the model prediction performance.The predicted compounds with high probability were used to reveal the potential differences in biological mechanisms between cold and hot properties.The cell proliferation-toxicity test was carried out to examine the protective effect of the compounds with a cold property predicted based on the weighted score on PC-12 cells exposed to oxygen-glucose deprivation and reperfusion(OGD/R).Results:The GCN model performed well in the prediction of cold/hot properties of Chinese herbal medicines.The compounds with a high probability of cold and hot property classification screened by the GCN model from the representative cold/hot medicines showed a total of 413 targets,which were associated with 17 pathways.The cell experiment results showed that the components with a cold property demonstrated a protective effect on the PC-12 cells exposed to OGD/R as the weighted score decreased.Conclusion:In the cold/hot property prediction task,the GCN model based on molecular graph representation outperforms the conventional machine learning model based on molecular fingerprint representation,which can provide algorithm support for exploring the“property-structure”relationship of Chinese herbal medicines.
作者
张冰冰
朱琦
张晶新
芦煜
王曦廷
卢涛
ZHANG Bingbing;ZHU Qi;ZHANG Jingxin;LU Yu;WANG Xiting;LU Tao(School of Life Science,Beijing University of Chinese Medicine,Beijing 102401,China;Institute of Information on Traditional Chinese Medicine,China Academy of Chinese Medical Sciences,Beijing 100010,China;Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China)
出处
《世界中医药》
CAS
2023年第21期3047-3052,3059,共7页
World Chinese Medicine
基金
国家自然科学基金青年基金项目(82104739)。
关键词
中药
药性
寒热药性
属性预测
深度学习
图卷积神经网络
作用机制
Chinese herbal medicine
Medicinal property
Cold/hot property
Property prediction
Deep learning
Graph convolutional neural network
Mechanism of action
