Genetic algorithm optimization of point charges in force field development: challenges and insights.
Genetic algorithm optimization of point charges in force field development: challenges and insights.
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DOI：
10.1021/acs.jpca.5b00218
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
M. Ivanov;M. Talipov;Q. K. Timerghazin
中科院分区：
文献类型：
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作者：
M. Ivanov;M. Talipov;Q. K. Timerghazin
进化方法，如遗传算法（GAs），提供了强大的工具，优化的力场参数，特别是在同时拟合的力场条款对广泛的参考数据的情况下。然而，GA拟合的非键合相互作用参数，包括点电荷尚未在文献中进行探讨，可能是由于许多困难，甚至一个更简单的问题，最小二乘拟合的原子点电荷对参考分子静电势（MEP），这往往表明一个异常高的变化的拟合电荷上埋的原子。在这里，我们检查的最小二乘MEP点电荷拟合的GA方法的性能，并显示GA优化遭受从经典的埋原子效应的放大版本，产生高度分散的，但相关的解决方案。这种效果可以理解的线性独立，自然坐标的MEP拟合问题定义的最小二乘和海森矩阵的特征向量，这也是等价的协方差矩阵的特征向量评估的分散GA解决方案。遗传算法快速收敛相对于高曲率坐标定义的本征向量有关的领导项的多极展开，但有困难收敛相对于低曲率坐标，主要取决于埋原子电荷。当使用Hessian或协方差矩阵特征向量进行点电荷优化时，进化技术的性能显著提高，该方法具有显著的潜力用于固定电荷生物分子力场的进化优化。
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.