Evidence map›Paper›PMID 39606265›Full record

ArticleChinese herbal medicines2024

Network pharmacology for traditional Chinese medicine in era of artificial intelligence.

Weibo Zhao, Boyang Wang, Shao Li

Abstract read
In one paragraph

Article in Chinese herbal medicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. International journal of molecular sciences · 2026
    Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Network Pharmacology, Molecular Docking, andCurrent topics in medicinal chemistry · 2026
    Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Weibo ZhaoInstitute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China.
Boyang WangInstitute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China.
Shao LiInstitute for TCM-X, Department of Automation, Tsinghua University, 100084 Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese Medicine Network Pharmacology (TCM-NP) is an interdisciplinary discipline that integrates information science, systems biology, network science and pharmacology, providing a systematic research methodology for TCM studies. With the development of artificial intelligence (AI) and multi-omics technologies, TCM-NP has entered a new era and can incorporate multimodal and high-dimensional data in the context of big data to enhance both theoretical foundations and technical capabilities. Despite its advancement, TCM-NP still faces challenges, particularly in ensuring the quality of data and research, as well as achieving more profound scientific discoveries. The field needs further innovation to obtain more precise and biomedically meaningful results. Overall research progress in TCM-NP depends on developing more accurate algorithms together with utilizing higher-quality and larger-scale data. This paper gives a perspective on the trends and characteristics of TCM-NP development and application in the era of AI.

Indexed as

artificial intelligencenetwork pharmacologytraditional Chinese medicine

Identifiers

PMID39606265
PMCPMC11589279

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.