Evidence map›Paper›PMID 41069865›Full record

ReviewJournal of pharmaceutical analysis2025

Research progress and challenges of molecular recognition techniques in the screening of active ingredients in Traditional Chinese medicine.

Lin Li, Qi Li, Yanxiao Li, Dandan Gong, Bonian Zhao

Abstract readReview
In one paragraph

Review in Journal of pharmaceutical analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

5 authors.

Lin LiInstitute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Qi LiCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Yanxiao LiCollege of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Dandan GongInstitute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Bonian ZhaoInstitute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese medicine (TCM) has become an important treasure trove of natural resources for the development of new medicines due to their diverse compositions, significant therapeutic effects, and few side effects. The screening of active ingredients in TCM represents a crucial step in elucidating the material basis and mechanism of action of TCM. At present, efficient and precise molecular recognition techniques based on intermolecular interactions have been extensively employed for the identification of active ingredients in TCM. This paper presents a review of the fundamental principles underlying solution-phase/affinity ligand fishing, solid-phase/affinity ligand fishing, molecular imprinting and molecular docking techniques, with a particular focus on their applications in the screening of active ingredients in TCM. Furthermore, the paper compares the advantages and disadvantages of the various techniques and identifies the limitations of existing techniques. In conclusion, the paper identifies the prospective trajectory of molecular recognition techniques in the domain of TCM research. This paper not only provides theoretical references for the development of new methods of active ingredient screening but also helps to promote the modernization and internationalization of TCM.

Indexed as

Active ingredientsMolecular recognitionScreeningTraditional Chinese medicine

Identifiers

PMID41069865
PMCPMC12506477

What OpenQuestion holds

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Registered trials

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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.