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ClogPalk: a method for predicting alkane/water partition coefficient

Overview of attention for article published in Perspectives in Drug Discovery and Design, June 2013
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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 (#36 of 949)
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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2 blogs
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3 X users

Citations

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32 Dimensions

Readers on

mendeley
45 Mendeley
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1 CiteULike
Title
ClogPalk: a method for predicting alkane/water partition coefficient
Published in
Perspectives in Drug Discovery and Design, June 2013
DOI 10.1007/s10822-013-9655-5
Pubmed ID
Authors

Peter W. Kenny, Carlos A. Montanari, Igor M. Prokopczyk

Abstract

Alkane/water partition coefficients (P(alk)) are less familiar to the molecular design community than their 1-octanol/water equivalents and access to both data and prediction tools is much more limited. A method for predicting alkane/water partition coefficient from molecular structure is introduced. The basis for the ClogP(alk) model is the strong (R² = 0.987) relationship between alkane/water partition coefficient and molecular surface area (MSA) that was observed for saturated hydrocarbons. The model treats a molecule as a perturbation of a saturated hydrocarbon molecule with the same MSA and uses increments defined for functional groups to quantify the extent to which logP(alk) is perturbed by the introduction each functional group. Interactions between functional groups, such as intramolecular hydrogen bonds are also parameterized within a perturbation framework. The functional groups and interactions between them are specified substructurally in a transparent and reproducible manner using SMARTS notation. The ClogP(alk) model was parameterized using data measured for structurally prototypical compounds that dominate the literature on alkane/water partition coefficients and then validated using an external test set of 100 alkane/water logP measurements, the majority of which were for drugs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 44 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 31%
Student > Ph. D. Student 6 13%
Student > Bachelor 4 9%
Student > Master 4 9%
Other 3 7%
Other 6 13%
Unknown 8 18%
Readers by discipline Count As %
Chemistry 18 40%
Agricultural and Biological Sciences 5 11%
Medicine and Dentistry 4 9%
Chemical Engineering 3 7%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 2 4%
Unknown 11 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 07 October 2018.
All research outputs
#2,043,983
of 25,457,858 outputs
Outputs from Perspectives in Drug Discovery and Design
#36
of 949 outputs
Outputs of similar age
#17,089
of 210,184 outputs
Outputs of similar age from Perspectives in Drug Discovery and Design
#1
of 6 outputs
Altmetric has tracked 25,457,858 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 96% 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 210,184 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 91% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them