Evidence map›Paper›PMID 29946351›Full record

ReviewChinese medicine2018

In silico approach in reveal traditional medicine plants pharmacological material basis.

Fan Yi, Li Li, Li-Jia Xu, Hong Meng, Yin-Mao Dong, Hai-Bo Liu, Pei-Gen Xiao

Abstract readReview
In one paragraph

Review in Chinese medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
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  10. Review
  11. Molecules (Basel, Switzerland) · 2022
    Article
  12. Article
  13. Recent efforts for drug identification from phytochemicals against SARS-CoV-2: Exploration of the chemical space to identify druggable leads.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2021
    Article
  14. Antioxidant-RichPlants (Basel, Switzerland) · 2021
    Article
  15. Article
  16. Article
  17. Article
  18. Review
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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

7 authors.

Fan Yi1Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People's Republic of China.ORCID 0000-0002-7476-616X
Li Li1Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People's Republic of China.
Li-Jia Xu3Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People's Republic of China.
Hong Meng1Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People's Republic of China.
Yin-Mao Dong1Key Laboratory of Cosmetic, China National Light Industry, Beijing Technology and Business University, No. 11/33, Fucheng Road, Haidian District, Beijing, 100048 People's Republic of China.
Hai-Bo Liu3Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People's Republic of China.
Pei-Gen Xiao3Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 151 Malianwa North Road, Haidian District, Beijing, 100193 People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, studies of traditional medicinal plants have gradually increased worldwide because the natural sources and variety of such plants allow them to complement modern pharmacological approaches. As computer technology has developed, in silico approaches such as virtual screening and network analysis have been widely utilized in efforts to elucidate the pharmacological basis of the functions of traditional medicinal plants. In the process of new drug discovery, the application of virtual screening and network pharmacology can enrich active compounds among the candidates and adequately indicate the mechanism of action of medicinal plants, reducing the cost and increasing the efficiency of the whole procedure. In this review, we first provide a detailed research routine for examining traditional medicinal plants by in silico techniques and elaborate on their theoretical principles. We also survey common databases, software programs and website tools that can be used for virtual screening and pharmacological network construction. Furthermore, we conclude with a simple example that illustrates the whole methodology, and we present perspectives on the development and application of this in silico methodology to reveal the pharmacological basis of the effects of traditional medicinal plants.

Indexed as

Medicinal plants information analyticsNetwork pharmacologyPharmacological basisTraditional medicinal plantsVirtual screening

Identifiers

PMID29946351
PMCPMC6006786

What OpenQuestion holds

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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.