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Label-free drug discovery

Overview of attention for article published in Frontiers in Pharmacology, March 2014
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Title
Label-free drug discovery
Published in
Frontiers in Pharmacology, March 2014
DOI 10.3389/fphar.2014.00052
Pubmed ID
Authors

Ye Fang

Abstract

Current drug discovery is dominated by label-dependent molecular approaches, which screen drugs in the context of a predefined and target-based hypothesis in vitro. Given that target-based discovery has not transformed the industry, phenotypic screen that identifies drugs based on a specific phenotype of cells, tissues, or animals has gained renewed interest. However, owing to the intrinsic complexity in drug-target interactions, there is often a significant gap between the phenotype screened and the ultimate molecular mechanism of action sought. This paper presents a label-free strategy for early drug discovery. This strategy combines label-free cell phenotypic profiling with computational approaches, and holds promise to bridge the gap by offering a kinetic and holistic representation of the functional consequences of drugs in disease relevant cells that is amenable to mechanistic deconvolution.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 3%
United States 1 1%
Unknown 85 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 27%
Researcher 16 18%
Student > Master 15 17%
Student > Doctoral Student 7 8%
Student > Bachelor 4 4%
Other 11 12%
Unknown 12 13%
Readers by discipline Count As %
Chemistry 14 16%
Biochemistry, Genetics and Molecular Biology 13 15%
Pharmacology, Toxicology and Pharmaceutical Science 12 13%
Agricultural and Biological Sciences 10 11%
Engineering 8 9%
Other 17 19%
Unknown 15 17%
Attention Score in Context

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 21 April 2021.
All research outputs
#19,944,091
of 25,373,627 outputs
Outputs from Frontiers in Pharmacology
#8,471
of 19,717 outputs
Outputs of similar age
#166,548
of 238,079 outputs
Outputs of similar age from Frontiers in Pharmacology
#33
of 70 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 19,717 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 49th percentile – i.e., 49% 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 238,079 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.