Stochastic walk-on-spheres(WOS)algorithms for solving the linearized Poisson-Boltzmann equation(LPBE)provide several attractive features not available in traditional deterministic solvers:Gaussian error bars can be co...
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Stochastic walk-on-spheres(WOS)algorithms for solving the linearized Poisson-Boltzmann equation(LPBE)provide several attractive features not available in traditional deterministic solvers:Gaussian error bars can be computed easily,the algorithm is readily parallelized and requires minimal memory and multiple solvent environments can be accounted for by reweighting ***,previouslyreported computational times of these Monte Carlo methods were not competitive with existing deterministic numerical *** present paper demonstrates a series of numerical optimizations that collectively make the computational time of these Monte Carlo LPBE solvers competitive with deterministic *** optimization techniques used are to ensure that each atom’s contribution to the variance of the electrostatic solvation free energy is the same,to optimize the bias-generating parameters in the algorithm and to use an epsilon-approximate rather than exact nearest-neighbor search when determining the size of the next step in the Brownian motion when outside the molecule.
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