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QEX: target-specific druglikeness filter enhances ligand-based virtual screening

Overview of attention for article published in Molecular Diversity, July 2018
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Title
QEX: target-specific druglikeness filter enhances ligand-based virtual screening
Published in
Molecular Diversity, July 2018
DOI 10.1007/s11030-018-9842-3
Pubmed ID
Authors

Masahiro Mochizuki, Shogo D. Suzuki, Keisuke Yanagisawa, Masahito Ohue, Yutaka Akiyama

Abstract

Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 21%
Student > Master 5 15%
Student > Bachelor 4 12%
Student > Ph. D. Student 3 9%
Professor 2 6%
Other 5 15%
Unknown 7 21%
Readers by discipline Count As %
Chemistry 7 21%
Agricultural and Biological Sciences 4 12%
Computer Science 4 12%
Pharmacology, Toxicology and Pharmaceutical Science 3 9%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 4 12%
Unknown 9 27%
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 10 July 2018.
All research outputs
#14,366,938
of 24,144,324 outputs
Outputs from Molecular Diversity
#262
of 516 outputs
Outputs of similar age
#173,590
of 331,797 outputs
Outputs of similar age from Molecular Diversity
#2
of 11 outputs
Altmetric has tracked 24,144,324 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 516 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 48th percentile – i.e., 48% 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 331,797 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.