Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights

Authors

Maxim Vadimovich Ivanov, Marquette University
Marat R. Talipov, Marquette UniversityFollow
Qadir K. Timerghazin, Marquette UniversityFollow

Document Type

Article

Language

eng

Format of Original

13 p.

Publication Date

2015

Publisher

American Chemical Society

Source Publication

Journal of Physical Chemistry A

Source ISSN

1089-5639

Original Item ID

DOI: 10.1021/acs.jpca.5b00218

Abstract

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.

Comments

Accepted version. Journal of Physical Chemistry A, Vol. 119, No. 8 (2015): 1422-1434. DOI. © 2015 American Chemical Society. Used with permission.

Recommended Citation

Ivanov, Maxim Vadimovich; Talipov, Marat R.; and Timerghazin, Qadir K., "Genetic Algorithm Optimization of Point Charges in Force Field Development: Challenges and Insights" (2015). Chemistry Faculty Research and Publications. 502.
https://epublications.marquette.edu/chem_fac/502