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Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method

Overview of attention for article published in Journal of Cheminformatics, January 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#44 of 220)
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

blogs
2 blogs

Citations

dimensions_citation
90 Dimensions

Readers on

mendeley
70 Mendeley
citeulike
2 CiteULike
Title
Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method
Published in
Journal of Cheminformatics, January 2011
DOI 10.1186/1758-2946-3-2
Pubmed ID
Authors

Michael P Mazanetz, Osamu Ichihara, Richard J Law, Mark Whittaker

Abstract

The reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
United Kingdom 2 3%
Germany 2 3%
Japan 2 3%
Brazil 1 1%
Unknown 59 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 34%
Student > Ph. D. Student 14 20%
Student > Master 6 9%
Other 5 7%
Professor 4 6%
Other 10 14%
Unknown 7 10%
Readers by discipline Count As %
Chemistry 36 51%
Agricultural and Biological Sciences 9 13%
Computer Science 5 7%
Medicine and Dentistry 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Other 4 6%
Unknown 9 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 08 March 2012.
All research outputs
#249,089
of 3,684,317 outputs
Outputs from Journal of Cheminformatics
#44
of 220 outputs
Outputs of similar age
#8,011
of 85,780 outputs
Outputs of similar age from Journal of Cheminformatics
#6
of 13 outputs
Altmetric has tracked 3,684,317 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 220 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done well, scoring higher than 80% 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 85,780 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 13 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 53% of its contemporaries.