%0 DATA
%A Maxim
V., Ivanov
%A Marat R., Talipov
%A Qadir K., Timerghazin
%D 2015
%T Genetic Algorithm Optimization of Point Charges in
Force Field Development: Challenges and Insights
%U https://acs.figshare.com/articles/journal_contribution/Genetic_Algorithm_Optimization_of_Point_Charges_in_Force_Field_Development_Challenges_and_Insights/2192362
%R 10.1021/acs.jpca.5b00218.s001
%2 https://acs.figshare.com/ndownloader/files/3826654
%K force field terms
%K force field parameters
%K point charges
%K nonbonded interaction parameters
%K point charge optimization
%K MEP
%K Genetic Algorithm Optimization
%K covariance matrix eigenvectors
%K GA
%K Force Field Development
%K coordinate
%X Evolutionary methods, such as genetic
algorithms (GAs), provide
powerful tools for optimization of the force field parameters, especially
in the case of simultaneous fitting of the force field terms against
extensive reference data. However, GA fitting of the nonbonded interaction
parameters that includes point charges has not been explored in the
literature, likely due to numerous difficulties with even a simpler
problem of the least-squares fitting of the atomic point charges against
a reference molecular electrostatic potential (MEP), which often demonstrates
an unusually high variation of the fitted charges on buried atoms.
Here, we examine the performance of the GA approach for the least-squares
MEP point charge fitting, and show that the GA optimizations suffer
from a magnified version of the classical buried atom effect, producing
highly scattered yet correlated solutions. This effect can be understood
in terms of the linearly independent, natural coordinates of the MEP
fitting problem defined by the eigenvectors of the least-squares sum
Hessian matrix, which are also equivalent to the eigenvectors of the
covariance matrix evaluated for the scattered GA solutions. GAs quickly
converge with respect to the high-curvature coordinates defined by
the eigenvectors related to the leading terms of the multipole expansion,
but have difficulty converging with respect to the low-curvature coordinates
that mostly depend on the buried atom charges. The performance of
the evolutionary techniques dramatically improves when the point charge
optimization is performed using the Hessian or covariance matrix eigenvectors,
an approach with a significant potential for the evolutionary optimization
of the fixed-charge biomolecular force fields.