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In silico approach in reveal traditional medicine plants pharmacological material basis

Overview of attention for article published in Chinese Medicine, June 2018
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1 tweeter

Citations

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Readers on

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188 Mendeley
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Title
In silico approach in reveal traditional medicine plants pharmacological material basis
Published in
Chinese Medicine, June 2018
DOI 10.1186/s13020-018-0190-0
Pubmed ID
Authors

Fan Yi, Li, Li-jia Xu, Hong Meng, Yin-mao Dong, Hai-bo Liu, Pei-gen Xiao

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.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 188 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 16%
Student > Bachelor 18 10%
Researcher 16 9%
Student > Master 15 8%
Unspecified 14 7%
Other 39 21%
Unknown 55 29%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 30 16%
Biochemistry, Genetics and Molecular Biology 23 12%
Agricultural and Biological Sciences 23 12%
Unspecified 14 7%
Chemistry 11 6%
Other 29 15%
Unknown 58 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 July 2018.
All research outputs
#20,523,725
of 23,094,276 outputs
Outputs from Chinese Medicine
#443
of 557 outputs
Outputs of similar age
#287,391
of 328,045 outputs
Outputs of similar age from Chinese Medicine
#6
of 7 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 557 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,045 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.