Electrostatics is of paramount importance to chemistry, physics, biology, and
medicine. The Poisson-Boltzmann (PB) theory is a primary model for
electrostatic analysis. However, it is highly challenging to compute accurate
PB electrostatic solvation free energies for macromolecules due to the
nonlinearity, dielectric jumps, charge singularity , and geometric complexity
associated with the PB equation. The present work introduces a PB based machine
learning (PBML) model for biomolecular electrostatic analysis. Trained with the
second-order accurate MIBPB solver, the proposed PBML model is found to be more
accurate and faster than several eminent PB solvers in electrostatic analysis.
The proposed PBML model can provide highly accurate PB electrostatic solvation
free energy of new biomolecules or new conformations generated by molecular
dynamics with much reduced computational cost