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Expanding the fragrance chemical space for virtual screening

Overview of attention for article published in Journal of Cheminformatics, May 2014
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Title
Expanding the fragrance chemical space for virtual screening
Published in
Journal of Cheminformatics, May 2014
DOI 10.1186/1758-2946-6-27
Pubmed ID
Authors

Lars Ruddigkeit, Mahendra Awale, Jean-Louis Reymond

Abstract

The properties of fragrance molecules in the public databases SuperScent and Flavornet were analyzed to define a "fragrance-like" (FL) property range (Heavy Atom Count ≤ 21, only C, H, O, S, (O + S) ≤ 3, Hydrogen Bond Donor ≤ 1) and the corresponding chemical space including FL molecules from PubChem (NIH repository of molecules), ChEMBL (bioactive molecules), ZINC (drug-like molecules), and GDB-13 (all possible organic molecules up to 13 atoms of C, N, O, S, Cl). The FL subsets of these databases were classified by MQN (Molecular Quantum Numbers, a set of 42 integer value descriptors of molecular structure) and formatted for fast MQN-similarity searching and interactive exploration of color-coded principal component maps in form of the FL-mapplet and FL-browser applications freely available at http://www.gdb.unibe.ch. MQN-similarity is shown to efficiently recover 15 different fragrance molecule families from the different FL subsets, demonstrating the relevance of the MQN-based tool to explore the fragrance chemical space.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Greece 1 2%
Germany 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 22%
Researcher 14 22%
Student > Master 9 14%
Student > Bachelor 5 8%
Other 3 5%
Other 10 16%
Unknown 8 13%
Readers by discipline Count As %
Chemistry 22 35%
Computer Science 6 10%
Agricultural and Biological Sciences 5 8%
Biochemistry, Genetics and Molecular Biology 4 6%
Chemical Engineering 3 5%
Other 12 19%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 June 2020.
All research outputs
#13,409,212
of 22,756,196 outputs
Outputs from Journal of Cheminformatics
#659
of 828 outputs
Outputs of similar age
#110,475
of 226,264 outputs
Outputs of similar age from Journal of Cheminformatics
#11
of 21 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 828 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 19th percentile – i.e., 19% 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 226,264 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 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.