Implicit
solvent models are one of the standard tools in computational
biophysics. While Poisson–Boltzmann methods offer highly accurate
results within this framework, generalized Born models have been used
due to their higher computational efficiency in many (bio)molecular
simulations, where computational power is a limiting factor. In recent
years, there have been remarkable advances to reduce some deficiencies
in the generalized Born models. On the other hand, these advances
come at an increased computational cost that contrasts the reasons
for choosing generalized Born models over Poisson–Boltzmann
methods. To address this performance issue, we present a new algorithm
for Born radii computation, one performance critical part in the evaluation
of generalized Born models, which is based on a Barnes–Hut
tree code scheme. We show that an implementation of this algorithm
provides accurate Born radii and polar solvation free energies in
comparison to Poisson–Boltzmann computations, while delivering
up to an order of magnitude better performance over existing, similarly
accurate methods. The C++ implementation of this algorithm will be
available at http://www.int.kit.edu/nanosim/