Accurate and Scalable Continuum Electrostatics for Large Biomolecular Systems: The pyDelPhi 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 pyDelPhi, 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, pyDelPhi preserves the established DelPhi 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 pyDelPhi reproduces DelPhi 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 DelPhi baseline while maintaining close numerical agreement. These results establish pyDelPhi as a unified and extensible platform that advances continuum electrostatics toward reproducible, high-performance modeling of biomolecular systems at both routine and large scales.