数字中医药（英文）2026，Vol.9Issue(2)：265-277,13.DOI:10.1016/j.dcmed.2026.05.009
HerbGL:一种基于网络传播与图正则化的中药药对预测框架
HerbGL:a network propagation and graph regularization-based framework for herb pairs prediction in traditional Chinese medicine
摘要
Abstract
Objective To address the challenges of systematically identifying herb pairs in traditional Chinese medicine(TCM),we proposed HerbGL,a framework for predicting potential herb pairs that integrates network propagation and graph regularization. Methods Based on the assumption that herbal actions induce subtle perturbations in biologi-cal systems,a framework named HerbGL was proposed.Random walk with restart(RWR)was first applied to the protein-protein interaction(PPI)network to reconstruct herb-specific per-turbation effects and generate weighted subnetworks.Then,to quantify affinity between herb pairs,two network-proximity metrics,Closeness and PageRank,were computed from the weighted subnetworks to construct herb-pair affinity matrices.Finally,these matrices,togeth-er with known herb pairs(derived from co-occurrence analysis of TCM formulas with a threshold determined from the inflection point of the frequency distribution),were incorpo-rated into a graph regularization model to predict potential herb pairs.Model performance was assessed through baseline comparison,ablation and robustness experiment under differ-ent ratios of positive and negative samples,using the area under the receiver operating char-acteristic curve(AUROC),the area under the precision-recall curve(AUPRC),accuracy,and precision as evaluation metrics.Furthermore,the predicted herb pairs were validated through both literature evidence and Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses. Results The weighted subnetworks constructed by RWR provided a refined simulation of herb-specific perturbation effects,which formed the basis for subsequent affinity modeling and prediction.Analysis of herb pair co-occurrence frequencies revealed a marked change around 150,which was selected as the threshold to distinguish herb pairs from non-herb pairs.HerbGL exhibited superior predictive performance compared with baseline models(AUROC=0.970 5,AUPRC=0.955 5,accuracy=0.726 6,precision=0.970 6).Ablation results showed that removing the Closeness and PageRank metrics substantially degraded perfor-mance(AUROC=0.819 1,AUPRC=0.876 8),confirming their necessity.Robustness evalua-tion under an imbalanced positive-to-negative sample ratio of 1:5 yielded AUROC=0.969 6 and AUPRC=0.840 4,indicating stable predictive ability.Moreover,multiple case studies fur-ther validated the rationality of the predicted herb pairs,such as Fangfeng(Saposhnikoviae Radix)and Qingpi(Citri Reticulatae Pericarpium Viride)which are recorded in Liangpeng Huiji(《良朋汇集》,Collection of Excellent Recipes)Vol.3:Fangfeng Shengma Tang(防风升麻汤).Additionally,pathway enrichment analysis of the Renshen(Ginseng Radix et Rhizoma)and Lianqiao(Forsythiae Fructus)pair further supported the biological plausibility of their compatibility. Conclusion HerbGL offers an effective and biologically informed framework for identifying herb pairs in TCM.Beyond improving herb pair prediction,the framework also provides data support for research on herb compounds and mechanisms,thereby supporting data-driven exploration of TCM compatibility.关键词
中医药/药对/网络传播/图正则化/网络药理学Key words
Traditional Chinese medicine/Herb pair/Network propagation/Graph regularization/Network pharmacology引用本文复制引用
刘为相,袁倩,张峻杰,孙鑫亮,胡孔法,杨涛..HerbGL:一种基于网络传播与图正则化的中药药对预测框架[J].数字中医药（英文）,2026,9(2):265-277,13.基金项目
National Natural Science Foundation of China(82575255),Open Research Project of Jiangsu Provincial Research Institute of Chinese Medicine Schools(JSZYLP2024060),and Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2156). （82575255）
