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dc.contributor.authorHuggins, Daviden
dc.date.accessioned2014-07-10T13:49:14Z
dc.date.available2014-07-10T13:49:14Z
dc.date.issued2014-05-19en
dc.identifier.citationHuggins, D,J. (2014) "Estimating Translational and Orientational Entropies Using the k-Nearest Neighbours Algorithm" Journal of Chemical Theory and Computation Just Accepted Manuscripten
dc.identifier.issn1549-9618
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/245446
dc.description.abstractInhomogeneous fluid solvation theory (IFST) and free energy perturbation (FEP) calculations were performed for a set of twenty solutes to compute the hydration free energies. We identify the weakness of histogram methods in computing the IFST hydration entropy by showing that previously employed histogram methods overestimate the translational and orientational entropies and thus underestimate their contribution to the free energy by a significant amount. Conversely, we demonstrate the accuracy of the k-nearest neighbours (KNN) algorithm in computing these translational and orientational entropies. Implementing the KNN algorithm within the IFST framework produces a powerful method that can be used to calculate free-energy changes for large perturbations. We introduce a new KNN approach to compute the total solute-water entropy with six degrees of freedom, as well as the translational and orientational contributions. However, results suggest that both the solute-water and water-water entropy terms are significant and must be included. When they are combined, the IFST and FEP hydration free energies are highly correlated, with an R2 of 0.999 and a mean unsigned difference of 0.9 kcal/mol. In summary, the KNN algorithm is shown to yield accurate estimates of the combined translational-orientational entropy and the novel approach of combining distance metrics that is developed here could be extended to provide a powerful method for entropy estimation in numerous contexts.
dc.description.sponsorshipThis work was supported by the MRC under grant ML/L007266/1. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/) provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and were funded by the EPSRC under grants EP/F032773/1 and EP/J017639/1.
dc.languageEnglishen
dc.language.isoenen
dc.publisherAmerican Chemical Society
dc.rightsAttribution 2.0 UK: England & Wales
dc.rightsCreative Commons Attribution License 2.0 UK
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.titleEstimating Translational and Orientational Entropies Using the k-Nearest Neighbours Algorithmen
dc.typeArticle
dc.description.versionThis is the final published version. It's also available from the journal website can be found at: http://pubs.acs.org/doi/abs/10.1021/ct500415gen
prism.publicationDate2014en
prism.publicationNameJournal of Chemical Theory and Computationen
dc.rioxxterms.funderMRC
dc.rioxxterms.funderEPSRC
dc.rioxxterms.projectidML/L007266/1
dc.rioxxterms.projectidEP/F032773/1
dc.rioxxterms.projectidEP/J017639/1
rioxxterms.versionofrecord10.1021/ct500415gen
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-05-19en
dc.contributor.orcidHuggins, David [0000-0003-1579-2496]
dc.identifier.eissn1549-9626
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/F032773/1)
pubs.funder-project-idEPSRC (EP/J017639/1)
pubs.funder-project-idMRC (MR/L007266/1)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales