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Quantitative estimation of pesticide-likeness for agrochemical discovery

Overview of attention for article published in Journal of Cheminformatics, September 2014
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Title
Quantitative estimation of pesticide-likeness for agrochemical discovery
Published in
Journal of Cheminformatics, September 2014
DOI 10.1186/s13321-014-0042-6
Pubmed ID
Authors

Sorin Avram, Simona Funar-Timofei, Ana Borota, Sridhar Rao Chennamaneni, Anil Kumar Manchala, Sorel Muresan

Abstract

The design of chemical libraries, an early step in agrochemical discovery programs, is frequently addressed by means of qualitative physicochemical and/or topological rule-based methods. The aim of this study is to develop quantitative estimates of herbicide- (QEH), insecticide- (QEI), fungicide- (QEF), and, finally, pesticide-likeness (QEP). In the assessment of these definitions, we relied on the concept of desirability functions.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 <1%
Romania 1 <1%
Switzerland 1 <1%
Unknown 106 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 27%
Researcher 22 20%
Student > Master 12 11%
Student > Bachelor 4 4%
Professor 4 4%
Other 14 13%
Unknown 24 22%
Readers by discipline Count As %
Chemistry 33 30%
Agricultural and Biological Sciences 27 25%
Biochemistry, Genetics and Molecular Biology 8 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 4%
Computer Science 3 3%
Other 6 6%
Unknown 28 26%
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 17 September 2014.
All research outputs
#18,835,246
of 23,340,595 outputs
Outputs from Journal of Cheminformatics
#829
of 862 outputs
Outputs of similar age
#175,232
of 244,709 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 9 outputs
Altmetric has tracked 23,340,595 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 862 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 1st percentile – i.e., 1% 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 244,709 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.