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Estimating Translational and Orientational Entropies Using the k-Nearest Neighbors Algorithm.

David J Huggins¹

¹Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge , 19 J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom.

Journal of Chemical Theory and Computation

|November 21, 2015

Summary

The k-nearest neighbors (KNN) algorithm accurately estimates hydration entropy, improving inhomogeneous fluid solvation theory (IFST) calculations. This method enhances free energy predictions for solutes, showing strong correlation with experimental data.

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Area of Science:

Computational chemistry
Physical chemistry
Molecular modeling

Background:

Inhomogeneous fluid solvation theory (IFST) and free energy perturbation (FEP) are crucial for calculating hydration free energies.
Traditional histogram methods often overestimate translational and orientational entropies in IFST, leading to inaccurate free energy contributions.

Purpose of the Study:

To evaluate the accuracy of the k-nearest neighbors (KNN) algorithm for computing IFST hydration entropy.
To introduce a novel KNN approach for calculating total solute-water entropy and its components.
To assess the combined accuracy of IFST and FEP methods with improved entropy calculations.

Main Methods:

Performed IFST and FEP calculations for 20 solutes.
Applied the k-nearest neighbors (KNN) algorithm to compute translational and orientational entropies.
Developed a new KNN approach to calculate total solute-water entropy (six degrees of freedom).
Included both solute-water and water-water entropy terms in the calculations.

Main Results:

The KNN algorithm accurately computes translational and orientational entropies, unlike histogram methods.
Combined IFST and FEP hydration free energies showed high correlation (R(2) = 0.999) with a small mean unsigned difference (0.9 kcal/mol).
IFST predictions correlated highly with experimental hydration free energies (R(2) = 0.997, mean unsigned error = 1.2 kcal/mol).

Conclusions:

The KNN algorithm provides accurate estimates of combined translational-orientational entropy.
The novel KNN approach for entropy estimation shows promise for various applications in computational chemistry.
Accurate entropy calculations are essential for reliable free energy predictions in solvation studies.