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Accuracy enhancement in the estimation of molecular hydration free energies by implementing the intramolecular hydrogen bond effects

Overview of attention for article published in Journal of Cheminformatics, November 2015
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Title
Accuracy enhancement in the estimation of molecular hydration free energies by implementing the intramolecular hydrogen bond effects
Published in
Journal of Cheminformatics, November 2015
DOI 10.1186/s13321-015-0106-2
Pubmed ID
Authors

Kee-Choo Chung, Hwangseo Park

Abstract

The formation of intramolecular hydrogen bonds (IHBs) may induce the remarkable changes in molecular physicochemical properties. Within the framework of the extended solvent-contact model, we investigate the effect of implementing the IHB interactions on the accuracy in estimating the molecular hydration free energies. The performances of hydration free energy functions including and excluding the IHB parameters are compared using the molecules distributed for SAMPL4 blind prediction challenge and those in Free Solvation Database (FSD). The calculated hydration free energies with IHB effects are found to be in considerably better agreement with the experimental data than those without them. For example, the root mean square error of the estimation decreases from 2.56 to 1.66 and from 1.73 to 1.54 kcal/mol for SAMPL4 and FSD molecules, respectively, due to the extension of atomic parameter space to cope with IHBs. These improvements are made possible by reducing the overestimation of attractive interactions between water and the solute molecules involving IHBs. The modified hydration free energy function is thus anticipated to be useful for estimating the desolvation cost for various organic molecules.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

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 %
United States 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 33%
Student > Ph. D. Student 1 11%
Professor 1 11%
Student > Bachelor 1 11%
Student > Master 1 11%
Other 2 22%
Readers by discipline Count As %
Chemistry 3 33%
Agricultural and Biological Sciences 2 22%
Business, Management and Accounting 1 11%
Biochemistry, Genetics and Molecular Biology 1 11%
Computer Science 1 11%
Other 1 11%

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 27 November 2015.
All research outputs
#9,839,009
of 12,320,043 outputs
Outputs from Journal of Cheminformatics
#451
of 485 outputs
Outputs of similar age
#219,222
of 324,330 outputs
Outputs of similar age from Journal of Cheminformatics
#22
of 24 outputs
Altmetric has tracked 12,320,043 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 485 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. 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 324,330 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
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