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What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics?

Overview of attention for article published in PLOS ONE, December 2012
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Title
What Can We Learn from the Evolution of Protein-Ligand Interactions to Aid the Design of New Therapeutics?
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0051742
Pubmed ID
Authors

Alicia P. Higueruelo, Adrian Schreyer, G. Richard J Bickerton, Tom L. Blundell, Will R. Pitt

Abstract

Efforts to increase affinity in the design of new therapeutic molecules have tended to lead to greater lipophilicity, a factor that is generally agreed to be contributing to the low success rate of new drug candidates. Our aim is to provide a structural perspective to the study of lipophilic efficiency and to compare molecular interactions created over evolutionary time with those designed by humans. We show that natural complexes typically engage in more polar contacts than synthetic molecules bound to proteins. The synthetic molecules also have a higher proportion of unmatched heteroatoms at the interface than the natural sets. These observations suggest that there are lessons to be learnt from Nature, which could help us to improve the characteristics of man-made molecules. In particular, it is possible to increase the density of polar contacts without increasing lipophilicity and this is best achieved early in discovery while molecules remain relatively small.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Germany 1 2%
India 1 2%
Brazil 1 2%
Japan 1 2%
United States 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 31%
Student > Ph. D. Student 14 24%
Student > Bachelor 6 10%
Student > Doctoral Student 4 7%
Other 3 5%
Other 9 16%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 34%
Chemistry 18 31%
Biochemistry, Genetics and Molecular Biology 9 16%
Computer Science 2 3%
Social Sciences 1 2%
Other 3 5%
Unknown 5 9%
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 30 September 2014.
All research outputs
#15,258,711
of 22,689,790 outputs
Outputs from PLOS ONE
#129,945
of 193,655 outputs
Outputs of similar age
#179,971
of 278,718 outputs
Outputs of similar age from PLOS ONE
#2,992
of 4,853 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193,655 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.0. This one is in the 24th percentile – i.e., 24% 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 278,718 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,853 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.