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Experimental validation of FINDSITEcomb virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders

Overview of attention for article published in Journal of Cheminformatics, April 2014
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 tweeters

Citations

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23 Dimensions

Readers on

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34 Mendeley
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Title
Experimental validation of FINDSITEcomb virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders
Published in
Journal of Cheminformatics, April 2014
DOI 10.1186/1758-2946-6-16
Pubmed ID
Authors

Bharath Srinivasan, Hongyi Zhou, Julia Kubanek, Jeffrey Skolnick

Abstract

Identification of ligand-protein binding interactions is a critical step in drug discovery. Experimental screening of large chemical libraries, in spite of their specific role and importance in drug discovery, suffer from the disadvantages of being random, time-consuming and expensive. To accelerate the process, traditional structure- or ligand-based VLS approaches are combined with experimental high-throughput screening, HTS. Often a single protein or, at most, a protein family is considered. Large scale VLS benchmarking across diverse protein families is rarely done, and the reported success rate is very low. Here, we demonstrate the experimental HTS validation of a novel VLS approach, FINDSITE(comb), across a diverse set of medically-relevant proteins.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Brazil 1 3%
Unknown 32 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 26%
Student > Ph. D. Student 8 24%
Student > Master 3 9%
Student > Postgraduate 2 6%
Professor 2 6%
Other 5 15%
Unknown 5 15%
Readers by discipline Count As %
Chemistry 9 26%
Biochemistry, Genetics and Molecular Biology 7 21%
Agricultural and Biological Sciences 6 18%
Chemical Engineering 1 3%
Mathematics 1 3%
Other 4 12%
Unknown 6 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 10 May 2014.
All research outputs
#7,497,754
of 13,001,121 outputs
Outputs from Journal of Cheminformatics
#410
of 521 outputs
Outputs of similar age
#83,299
of 189,761 outputs
Outputs of similar age from Journal of Cheminformatics
#4
of 10 outputs
Altmetric has tracked 13,001,121 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 521 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 16th percentile – i.e., 16% 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 189,761 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.