| Title |
Unraveling the performance of dispersion-corrected functionals for the accurate description of weakly bound natural polyphenols
|
|---|---|
| Published in |
Journal of Molecular Modeling, October 2015
|
| DOI | 10.1007/s00894-015-2838-3 |
| Pubmed ID | |
| Authors |
Florent Di Meo, Imene Bayach, Patrick Trouillas, Juan-Carlos Sancho-García |
| Abstract |
Long-range non-covalent interactions play a key role in the chemistry of natural polyphenols. We have previously proposed a description of supramolecular polyphenol complexes by the B3P86 density functional coupled with some corrections for dispersion. We couple here the B3P86 functional with the D3 correction for dispersion, assessing systematically the accuracy of the new B3P86-D3 model using for that the well-known S66, HB23, NCCE31, and S12L datasets for non-covalent interactions. Furthermore, the association energies of these complexes were carefully compared to those obtained by other dispersion-corrected functionals, such as B(3)LYP-D3, BP86-D3 or B3P86-NL. Finally, this set of models were also applied to a database composed of seven non-covalent polyphenol complexes of the most interest. Graphical abstract Weakly bound natural polyphenolsᅟ. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 11% |
| Czechia | 1 | 11% |
| Unknown | 7 | 78% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 2 | 22% |
| Student > Ph. D. Student | 2 | 22% |
| Researcher | 2 | 22% |
| Student > Doctoral Student | 1 | 11% |
| Unknown | 2 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 4 | 44% |
| Computer Science | 1 | 11% |
| Biochemistry, Genetics and Molecular Biology | 1 | 11% |
| Unknown | 3 | 33% |
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 21 September 2016.
All research outputs
#18,472,072
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Outputs from Journal of Molecular Modeling
#526
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#204,760
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Outputs of similar age from Journal of Molecular Modeling
#5
of 9 outputs
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