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A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

Overview of attention for article published in Frontiers in Neurology, September 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

blogs
1 blog
twitter
1 X user

Citations

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23 Dimensions

Readers on

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109 Mendeley
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Title
A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets
Published in
Frontiers in Neurology, September 2015
DOI 10.3389/fneur.2015.00197
Pubmed ID
Authors

Michael J. Wasko, Kendy A. Pellegrene, Jeffry D. Madura, Christopher K. Surratt

Abstract

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for "growing" the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

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

Geographical breakdown

Country Count As %
Netherlands 1 <1%
United States 1 <1%
Unknown 107 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 19%
Student > Master 19 17%
Student > Bachelor 15 14%
Researcher 14 13%
Other 7 6%
Other 16 15%
Unknown 17 16%
Readers by discipline Count As %
Chemistry 33 30%
Biochemistry, Genetics and Molecular Biology 23 21%
Agricultural and Biological Sciences 13 12%
Computer Science 5 5%
Medicine and Dentistry 5 5%
Other 11 10%
Unknown 19 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 30 December 2015.
All research outputs
#3,180,423
of 22,828,180 outputs
Outputs from Frontiers in Neurology
#2,620
of 11,711 outputs
Outputs of similar age
#44,248
of 267,781 outputs
Outputs of similar age from Frontiers in Neurology
#23
of 58 outputs
Altmetric has tracked 22,828,180 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,711 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 77% 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 267,781 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 58 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 60% of its contemporaries.