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High-quality and universal empirical atomic charges for chemoinformatics applications

Overview of attention for article published in Journal of Cheminformatics, December 2015
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Title
High-quality and universal empirical atomic charges for chemoinformatics applications
Published in
Journal of Cheminformatics, December 2015
DOI 10.1186/s13321-015-0107-1
Pubmed ID
Authors

Stanislav Geidl, Tomáš Bouchal, Tomáš Raček, Radka Svobodová Vařeková, Václav Hejret, Aleš Křenek, Ruben Abagyan, Jaroslav Koča

Abstract

Partial atomic charges describe the distribution of electron density in a molecule and therefore provide clues to the chemical behaviour of molecules. Recently, these charges have become popular in chemoinformatics, as they are informative descriptors that can be utilised in pharmacophore design, virtual screening, similarity searches etc. Especially conformationally-dependent charges perform very successfully. In particular, their fast and accurate calculation via the Electronegativity Equalization Method (EEM) seems very promising for chemoinformatics applications. Unfortunately, published EEM parameter sets include only parameters for basic atom types and they often miss parameters for halogens, phosphorus, sulphur, triple bonded carbon etc. Therefore their applicability for drug-like molecules is limited. We have prepared six EEM parameter sets which enable the user to calculate EEM charges in a quality comparable to quantum mechanics (QM) charges based on the most common charge calculation schemes (i.e., MPA, NPA and AIM) and a robust QM approach (HF/6-311G, B3LYP/6-311G). The calculated EEM parameters exhibited very good quality on a training set ([Formula: see text]) and also on a test set ([Formula: see text]). They are applicable for at least 95 % of molecules in key drug databases (DrugBank, ChEMBL, Pubchem and ZINC) compared to less than 60 % of the molecules from these databases for which currently used EEM parameters are applicable. We developed EEM parameters enabling the fast calculation of high-quality partial atomic charges for almost all drug-like molecules. In parallel, we provide a software solution for their easy computation (http://ncbr.muni.cz/eem_parameters). It enables the direct application of EEM in chemoinformatics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 3 4%
Unknown 67 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 17%
Student > Bachelor 10 14%
Researcher 8 11%
Student > Master 7 10%
Student > Doctoral Student 4 6%
Other 8 11%
Unknown 21 30%
Readers by discipline Count As %
Chemistry 18 26%
Computer Science 7 10%
Engineering 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Agricultural and Biological Sciences 2 3%
Other 13 19%
Unknown 23 33%
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 11 December 2015.
All research outputs
#15,351,847
of 22,835,198 outputs
Outputs from Journal of Cheminformatics
#750
of 834 outputs
Outputs of similar age
#227,309
of 387,649 outputs
Outputs of similar age from Journal of Cheminformatics
#10
of 13 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 834 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 5th percentile – i.e., 5% 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 387,649 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.