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Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery

Overview of attention for article published in Frontiers in Molecular Biosciences, June 2022
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Mentioned by

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1 X user

Citations

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5 Dimensions

Readers on

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9 Mendeley
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Title
Validation of Deep Learning-Based DFCNN in Extremely Large-Scale Virtual Screening and Application in Trypsin I Protease Inhibitor Discovery
Published in
Frontiers in Molecular Biosciences, June 2022
DOI 10.3389/fmolb.2022.872086
Pubmed ID
Authors

Haiping Zhang, Xiao Lin, Yanjie Wei, Huiling Zhang, Linbu Liao, Hao Wu, Yi Pan, Xuli Wu

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 11%
Researcher 1 11%
Student > Doctoral Student 1 11%
Unknown 6 67%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 1 11%
Medicine and Dentistry 1 11%
Unknown 7 78%
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 15 June 2022.
All research outputs
#20,165,369
of 22,675,759 outputs
Outputs from Frontiers in Molecular Biosciences
#2,466
of 3,694 outputs
Outputs of similar age
#354,994
of 438,947 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#229
of 385 outputs
Altmetric has tracked 22,675,759 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 3,694 research outputs from this source. They receive a mean Attention Score of 3.3. 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 438,947 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 385 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.