Accurate and Scalable Continuum Electrostatics for Large Biomolecular Systems: The <i>pyDelPhi</i> Poisson–Boltzmann Framework

Electrostatic interactions are central to biomolecular structure, recognition, and assembly, making their efficient and accurate evaluation essential for biophysics, drug discovery, and materials modeling. The Poisson-Boltzmann equation (PBE) provides a rigorous implicit-solvent description, yet large macromolecular systems remain challenging for conventional CPU-based solvers. Here, we introduce <i>pyDelPhi</i>, a modern finite-difference PBE framework designed for numerical accuracy, scalability, and reproducibility across heterogeneous architectures. Implemented in Python with just-in-time compilation via Numba, <i>pyDelPhi</i> preserves the established <i>DelPhi</i> numerical framework while supporting traditional, Gaussian-density, and regularized dielectric models and enabling multiprecision execution on CPUs and NVIDIA GPUs via a CUDA backend. Benchmarking across three curated data sets shows that <i>pyDelPhi</i> reproduces <i>DelPhi</i> reaction-field energies within fractions of a percent and achieves 7-20× GPU acceleration for the linearized PBE. A cuboidal grid-box option reduces memory by up to 80% and accelerates runtimes several-fold for anisotropic systems. A complete viral-capsid calculation (∼5 × 10⁵ atoms, 10⁹ grid points) yields an order-of-magnitude reduction in wall time relative to the single-core <i>DelPhi</i> baseline while maintaining close numerical agreement. These results establish <i>pyDelPhi</i> as a unified and extensible platform that advances continuum electrostatics toward reproducible, high-performance modeling of biomolecular systems at both routine and large scales.