Achieving all-atom molecular dynamics accuracy from the Poisson–Boltzmann method through machine learning

Abstract

All-atom molecular dynamics (MD) simulations are a standard tool for probing the structural and dynamical properties of biomolecular systems, but their accuracy comes at the cost of high computational demands. To overcome spatial–temporal limitations, implicit models or coarse-graining are often employed, but usually at the expense of reduced accuracy. This limitation is also evident in the Poisson–Boltzmann (PB) mean-field theory, which efficiently captures long-range electrostatics but fails to account for crucial short-range interactions. In this work, we bridge this gap by introducing a graph neural network (GNN) Δ-learning approach trained on the difference between all-atom MD and PB, resulting in DIS-PB (deep implicit solvation model using the PB potential as a prior). DIS-PB, which models solutes and salt ions explicitly by MD while water is coarse-grained out, captures both short-range electrostatic correlations as well as long-range electrostatic interaction tails. Applied to a system of the DNA molecule in 1 mol l−1 salt solution, our method reproduces structural properties (NDPs, RDFs, and binding probability patterns) with high fidelity, showing that the GNN-corrected PB can reach the accuracy of all-atom MD at a lower computational cost.