Library and Information Service

Library and Information Service >

2026 , Vol. 70 >Issue 6: 40 - 54

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.06.004

SPECIAL TOPIC: AI Enables the Inheritance and Innovation of Herbal Classics

Research on Core Drug Discovery Based on Knowledge Supernetwork of Herbal Classics

  • SHEN Wang ,
  • YU Lin ,
  • CHEN Xiaomei ,
  • SUN Ke ,
  • WEN Wenting
Expand
  • 1 School of Business and Management, Jilin University, Changchun 130012;
    2 College of Traditional Chinese Medicine·College of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210023
Shen Wang, Professor, PhD, Doctoral Supervisor, E-mail: shenwang_001@163.com; Yu Lin, Master’s Candidate; Chen Xiaomei, Associate Professor, PhD, Master’s Supervisor; Sun Ke, Master’s Candidate; Wen Wenting, Lecturer, PhD.

Received date: 2025-06-25

  Revised date: 2025-09-15

  Online published: 2026-03-14

Supported by

This work is supported by the Special Project of Unpopular Extinctive Subjects of the National Social Science Found of China, titled “Research on the Collection, Knowledge Organization and Intelligentization Construction of Classical Herb Books” (Grant No.23VJXT024).

Fold

Abstract

[Purpose/Significance] This study aims to employ a knowledge supernetwork to systematically organize and describe knowledge of herbal classics. Based on the content and structure of the supernetwork, this study identifies core drugs, thereby facilitating the transformation and application of the herbal classics knowledge. [Method/Process] This study systematically sorted out and organized the knowledge elements and their associations of herbal classics to construct a knowledge supernetwork model. Based on the supernetwork structure and quantitative characteristics, it designed the drug measurement index and core drug calculation method to identify core drugs. An empirical study was conducted using data from ancient books and medical cases. [Result/Conclusion] The results show that the constructed supernetwork model realizes the systematic organization and deep mining of herbal classics knowledge. The model successfully identifies core traditional Chinese medicine based on the supernetwork structure and quantitative characteristics. This study provides theoretical support and empirical basis for the modern transformation of traditional Chinese medicine knowledge.

Key words： herbal classics; knowledge supernetwork; core drugs; knowledge discovery

Cite this article

SHEN Wang , YU Lin , CHEN Xiaomei , SUN Ke , WEN Wenting . Research on Core Drug Discovery Based on Knowledge Supernetwork of Herbal Classics[J]. Library and Information Service, 2026 , 70(6) : 40 -54 . DOI: 10.13266/j.issn.0252-3116.2026.06.004

References

[1] 中共中央办公厅, 国务院办公厅. 关于推进新时代古籍工作的意见[EB/OL]. [2025-08-21]. https://www.gov.cn/zhengce/2022-04/11/content_5684555.htm. (The General Office of the Communist Party of China Central Committee, The General Office of the State Council. Opinions on advancing the management work on ancient books in the new era[EB/OL]. [2025-08-21]. https://www.gov.cn/zhengce/2022-04/11/content_5684555.htm.)
[2] 高国伟, 段佳琪, 李永先. 知识超网络研究综述[J]. 情报科学, 2018, 36(12): 162-167, 176. (GAO G W, DUAN J Q, LI Y X. Review of the research on knowledge supernetwork[J]. Information science, 2018, 36(12): 162-167, 176.)
[3] 沈洪, 叶柏, 张露, 等. 基于数据挖掘的溃疡性结肠炎核心药物及配伍分析[J]. 世界科学技术-中医药现代化, 2013, 15(5): 926-931. (SHEN H, YE B, ZHANG L, et al. Core drugs and compatibility analysis of ulcerative colitis based on data mining[J]. Modernization of traditional Chinese medicine and materia medica-world science and technology, 2013, 15(5): 926-931.)
[4] 任园园, 汤玉梅, 吴振起. 中医古籍哮喘用药规律的数据挖掘研究[J]. 中国中医急症, 2021, 30(3): 402-405. (REN Y Y, TANG Y M, WU Z Q. Medication rule of Chinese medicine on asthma based on the data mining of traditional Chinese medicine ancient books[J]. Journal of emergency in traditional Chinese medicine, 2021, 30(3): 402-405.)
[5] 张平, 刘雪峰, 吕文亮, 等. 古医籍疫病舌象临床用药规律探析[J]. 西部中医药, 2023, 36(11): 42-47. (ZHANG P, LIU X F, LYU W L, et al. Exploration into the rules of clinical medication based on the tongue manifestations of epidemic disease in ancient medical books[J]. Western journal of traditional Chinese medicine, 2023, 36(11): 42-47.)
[6] 雷鸣, 夏梦鸽, 汪雪锋, 等. 基于链路预测的协同药物组合推荐研究：面向疾病并发症诊疗[J]. 图书情报工作, 2021, 65(12): 122-129. (LEI M, XIA M G, WANG X F, et al. Research on drug combination recommendation based on link prediction for concurrent diseases treatment[J]. Library and information service, 2021, 65(12): 122-129.)
[7] 成全, 佘德昕. 融合患者体征与用药数据的图神经网络药物推荐方法研究[J]. 数据分析与知识发现, 2022, 6(9): 113-124. (CHENG Q, SHE D X. Drug recommendation based on graph neural network with patient signs and medication data[J]. Data analysis and knowledge discovery, 2022, 6(9): 113-124.)
[8] 吴佳伦, 张若楠, 康武林, 等. 基于患者相似性分析的药物推荐深度学习模型研究[J]. 数据分析与知识发现, 2023, 7(6): 148-160. (WU J L, ZHANG R N, KANG W L, et al. Deep learning model of drug recommendation based on patient similarity analysis[J]. Data analysis and knowledge discovery, 2023, 7(6): 148-160.)
[9] HEARN G, SCOTT D. Students staying home: questioning the wisdom of a digital future for Australian universities[J]. Futures, 1998, 30(7): 731-737.
[10] NAGURNEY A, DONG J. Supernetworks: decision-making for the information age[M]. Cheltenham: Edward Elgar Publishing, 2002.
[11] DONG J, NAGURNEY A. A supernetwork model for commuting versus telecommuting decision making[C] //Transportation planning and management in the 21st century. Hong Kong: Hong Kong Society for Transportation Studies, 2001: 386.
[12] 席运江, 党延忠, 廖开际. 组织知识系统的知识超网络模型及应用[J]. 管理科学学报, 2009, 12(3): 12-21. (XI Y J, DANG Y Z, LIAO K J. Knowledge supernetwork model and its application in organizational knowledge systems[J]. Journal of management sciences in China, 2009, 12(3): 12-21.)
[13] 王志平, 王众托. 超网络理论及其应用[M]. 北京: 科学出版社, 2008. (WANG Z P, WANG Z T. Hypernetwork theory and its applications[M]. Beijing: Science Press, 2008.)
[14] 于洋, 党延忠. 组织人才培养的超网络模型[J]. 系统工程理论与实践, 2009, 29(4): 154-160. (YU Y, DANG Y Z. Supernetwork model of personnel training in organization[J]. Systems engineering-theory & practice, 2009, 29(4): 154-160.)
[15] ZHAO L, ZHANG H, WU W. Cooperative knowledge creation in an uncertain network environment based on a dynamic knowledge supernetwork[J]. Scientometrics, 2019, 119(2): 657-685.
[16] 俞成诚, 王念, 关双, 等. 已上市中成药治疗肾阳虚证的“病-症-药”超网络分析及核心处方发现[J]. 中草药, 2023, 54(24): 8153-8164. (YU C C, WANG N, GUAN S, et al. “Disease-symptom-herb” supernetwork analysis and core formula discovery of postmarketing Chinese patent medicines for treatment of kidney-yang deficiency[J]. Chinese traditional and herbal drugs, 2023, 54(24): 8153-8164.)
[17] 符康, 闫光辉, 罗浩. 基于超图理论的中医方剂网络药对挖掘方法[J]. 计算机工程与应用, 2024, 60(10): 320-331. (FU K, YAN G H, LUO H. Hypergraph-Based network herbal pair mining method for traditional Chinese medicine[J]. Computer engineering and applications, 2024, 60(10): 320-331.)
[18] 单雨濛, 张科, 胡文军, 等. 基于超图的中药方剂超网络中药材群组信息挖掘[J]. 中草药, 2024, 55(11): 3816-3824. (SHAN Y M, ZHANG K, HU W J, et al. Mining of group information of medicinal materials in traditional Chinese medicine prescriptions hypernetwork based on hypergraph[J]. Chinese traditional and herbal drugs, 2024, 55(11): 3816-3824.)
[19] 徐浩. 构建“方—病—证”中医辨证论治新体系[J]. 中国中西医结合杂志, 2024, 44(12): 1503-1506. (XU H. Construction of “formula-disease-syndrome”: a new system of syndrome differentiation and treatment[J]. Chinese journal of integrated traditional and western medicine, 2024, 44(12): 1503-1506.)
[20] 周伟, 王峰, 王崇骏, 等. 利用效用度挖掘核心药物及配伍规律[J]. 计算机科学与探索, 2013, 7(11): 994-1001. (ZHOU W, WANG F, WANG C J, et al. Mining core herbs and their combination rules using effect degree[J]. Journal of frontiers of computer science and technology, 2013, 7(11): 994-1001.)
[21] 马宁, 郝秀霞, 白金牛, 等. 基于药物剂量及作用的方剂核心用药配伍规律复杂网络算法的构建[J]. 中医杂志, 2020, 61(1): 31-35. (MA N, HAO X X, BAI J N, et al. Construction of complex network algorithm for the core drug compatibility law of prescriptions based on drug dose and effect[J]. Journal of traditional Chinese medicine, 2020, 61(1): 31-35.)
[22] 杨勤军, 何振豪, 史友, 等. 基于症状—证候—药物的新安医家治疗肺胀医案数据挖掘研究[J]. 北京中医药大学学报, 2022, 45(6): 594-602. (YANG Q J, HE Z H, SHI Y, et al. Xin’an doctors’ records of treating lung distention based on the “symptoms-patterns-medicines” model: A data mining study[J]. Journal of Beijing University of traditional Chinese medicine, 2022, 45(6): 594-602.)
[23] 漏佳丽, 叶玲, 俞国尧. 基于复杂网络分析中药治疗耳鸣核心方药及配伍规律[J]. 光明中医, 2024, 39(21): 4241-4244. (LOU J L, YE L, YU G Y. Analysis on the core prescriptions and compatibility rules of Chinese medicine in the treatment of tinnitus based on complex network[J]. Guangming journal of Chinese medicine, 2024, 39(21): 4241-4244.)
[24] 孟庆鸿, 曾凤, 胡一帆, 等. 《金匮要略》黄芪用量考证及其量效关系探讨[J]. 中华中医药杂志, 2024, 39(8): 4055-4058. (MENG Q H, ZENG F, HU Y F, et al. Textual research on the dosage of astragali radix in synopsis of golden chamber and discussion on the relationship between its dosage and effect[J]. China journal of traditional Chinese medicine and pharmacy, 2024, 39(8): 4055-4058.)
[25] 刘毅, 王蕾, 李媛. 古代医籍《备急千金要方》药方文献源研究[J]. 图书馆工作与研究, 2011(4): 74-76. (LIU Y, WANG L, LI Y. Research on the document source of medical prescription in Bei Ji Qian Jin Fang[J]. Library work and study, 2011(4): 74-76.)
Options
Outlines

/

〈 〉

    Copyrights © LIS Press Co., Ltd. Reproduction is prohibited without permission.