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A novel descriptor based on atom-pair properties

Overview of attention for article published in Journal of Cheminformatics, January 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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1 blog
twitter
6 X users
googleplus
1 Google+ user

Citations

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45 Dimensions

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48 Mendeley
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Title
A novel descriptor based on atom-pair properties
Published in
Journal of Cheminformatics, January 2017
DOI 10.1186/s13321-016-0187-6
Pubmed ID
Authors

Masataka Kuroda

Abstract

Molecular descriptors have been widely used to predict biological activities and physicochemical properties or to analyze chemical libraries on the basis of similarity. Although fingerprints and properties are generally used as descriptors, neither is perfect for these purposes. A fingerprint can distinguish between molecules, whereas a property may not do the same in certain cases, and vice versa. When the number of the training set is especially small, the construction of good predictive models is difficult. Herein, a novel descriptor integrating mutually compensating fingerprint and property characteristics is described. The format of this descriptor is not conventional. It has two dimensions with variable length in one dimension to represent one molecule. This format is not acceptable for any machine learning methods. Therefore the distance between molecules has been newly defined for application to machine learning techniques. The evaluation of this descriptor, as applied to classification tasks, was performed using a support vector machine after the features of the descriptor had been optimized by a genetic algorithm. Because the optimizing feature is time-intensive due to the complicated calculation of distances between molecules, the optimization was forced to stop before it was completed. As a result, no remarkable improvement was observed in the classification results for the new descriptor compared with those for other descriptors in any evaluation set used in this work. However, extremely low accuracies were also not found for any set. The novel descriptor proposed in this work can potentially be used to make highly accurate predictive models. This new concept in descriptors is expected to be useful for developing novel predictive methods with quick training and high accuracy.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 47 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 29%
Researcher 11 23%
Student > Bachelor 6 13%
Student > Ph. D. Student 4 8%
Other 3 6%
Other 3 6%
Unknown 7 15%
Readers by discipline Count As %
Chemistry 14 29%
Pharmacology, Toxicology and Pharmaceutical Science 5 10%
Agricultural and Biological Sciences 4 8%
Chemical Engineering 4 8%
Biochemistry, Genetics and Molecular Biology 3 6%
Other 9 19%
Unknown 9 19%
Attention Score in Context

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 28 January 2020.
All research outputs
#2,647,715
of 22,950,943 outputs
Outputs from Journal of Cheminformatics
#266
of 840 outputs
Outputs of similar age
#55,820
of 421,147 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 19 outputs
Altmetric has tracked 22,950,943 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 840 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 68% 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 421,147 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 86% of its contemporaries.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.