Estimating Translational and Orientational Entropies Using the k-Nearest Neighbours Algorithm
Journal of Chemical Theory and Computation
American Chemical Society
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Huggins, D. (2014). Estimating Translational and Orientational Entropies Using the k-Nearest Neighbours Algorithm. Journal of Chemical Theory and Computation https://doi.org/10.1021/ct500415g
Inhomogeneous 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.
This 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.
External DOI: https://doi.org/10.1021/ct500415g
This record's URL: https://www.repository.cam.ac.uk/handle/1810/245446
Attribution 2.0 UK: England & Wales, Creative Commons Attribution License 2.0 UK
Licence URL: http://creativecommons.org/licenses/by/2.0/uk/