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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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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 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 29%
Researcher 6 10%
Student > Master 6 10%
Student > Doctoral Student 5 8%
Lecturer > Senior Lecturer 3 5%
Other 10 17%
Unknown 12 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 12 20%
Medicine and Dentistry 7 12%
Agricultural and Biological Sciences 7 12%
Pharmacology, Toxicology and Pharmaceutical Science 5 8%
Computer Science 4 7%
Other 12 20%
Unknown 12 20%

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
#11,697,437
of 13,171,980 outputs
Outputs from Chinese Medicine
#241
of 289 outputs
Outputs of similar age
#233,024
of 268,353 outputs
Outputs of similar age from Chinese Medicine
#1
of 1 outputs
Altmetric has tracked 13,171,980 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 289 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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