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A Bright Future for Evolutionary Methods in Drug Design

Overview of attention for article published in ChemMedChem, June 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
3 tweeters
facebook
2 Facebook pages

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
25 Mendeley
citeulike
1 CiteULike
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Title
A Bright Future for Evolutionary Methods in Drug Design
Published in
ChemMedChem, June 2015
DOI 10.1002/cmdc.201500161
Pubmed ID
Authors

Tu C. Le, David A. Winkler

Abstract

Most medicinal chemists understand that chemical space is extremely large, essentially infinite. Although high-throughput experimental methods allow exploration of drug-like space more rapidly, they are still insufficient to fully exploit the opportunities that such large chemical space offers. Evolutionary methods can synergistically blend automated synthesis and characterization methods with computational design to identify promising regions of chemical space more efficiently. We describe how evolutionary methods are implemented, and provide examples of published drug development research in which these methods have generated molecules with increased efficacy. We anticipate that evolutionary methods will play an important role in future drug discovery.

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

Geographical breakdown

Country Count As %
Brazil 1 4%
China 1 4%
Unknown 23 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 36%
Student > Master 4 16%
Professor 3 12%
Unspecified 3 12%
Professor > Associate Professor 2 8%
Other 4 16%
Readers by discipline Count As %
Chemistry 11 44%
Unspecified 7 28%
Agricultural and Biological Sciences 2 8%
Computer Science 2 8%
Social Sciences 1 4%
Other 2 8%

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 03 July 2016.
All research outputs
#7,068,527
of 12,524,647 outputs
Outputs from ChemMedChem
#922
of 2,077 outputs
Outputs of similar age
#100,086
of 234,676 outputs
Outputs of similar age from ChemMedChem
#21
of 79 outputs
Altmetric has tracked 12,524,647 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,077 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 53% of its peers.
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 234,676 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 55% of its contemporaries.
We're also able to compare this research output to 79 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.