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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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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 36 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 34 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 25%
Student > Ph. D. Student 8 22%
Student > Master 3 8%
Student > Bachelor 2 6%
Student > Postgraduate 2 6%
Other 5 14%
Unknown 7 19%
Readers by discipline Count As %
Chemistry 9 25%
Biochemistry, Genetics and Molecular Biology 7 19%
Agricultural and Biological Sciences 6 17%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Mathematics 1 3%
Other 4 11%
Unknown 8 22%
Attention Score in Context

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
#14,195,272
of 22,754,104 outputs
Outputs from Journal of Cheminformatics
#699
of 828 outputs
Outputs of similar age
#120,167
of 226,899 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 22 outputs
Altmetric has tracked 22,754,104 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 12th percentile – i.e., 12% 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 226,899 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.