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A confidence predictor for logD using conformal regression and a support-vector machine

Overview of attention for article published in Journal of Cheminformatics, April 2018
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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 (87th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

blogs
1 blog
twitter
17 tweeters

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
20 Mendeley
Title
A confidence predictor for logD using conformal regression and a support-vector machine
Published in
Journal of Cheminformatics, April 2018
DOI 10.1186/s13321-018-0271-1
Pubmed ID
Authors

Maris Lapins, Staffan Arvidsson, Samuel Lampa, Arvid Berg, Wesley Schaal, Jonathan Alvarsson, Ola Spjuth

Abstract

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 40%
Student > Ph. D. Student 6 30%
Student > Master 3 15%
Other 1 5%
Student > Bachelor 1 5%
Other 1 5%
Readers by discipline Count As %
Chemistry 6 30%
Agricultural and Biological Sciences 3 15%
Pharmacology, Toxicology and Pharmaceutical Science 3 15%
Unspecified 2 10%
Chemical Engineering 2 10%
Other 4 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 21 April 2018.
All research outputs
#808,402
of 12,519,146 outputs
Outputs from Journal of Cheminformatics
#83
of 502 outputs
Outputs of similar age
#34,147
of 273,251 outputs
Outputs of similar age from Journal of Cheminformatics
#2
of 12 outputs
Altmetric has tracked 12,519,146 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 502 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has done well, scoring higher than 83% 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 273,251 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 87% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.