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arXiv · 2312.11482

Poisson-Boltzmann based machine learning (PBML) model for electrostatic analysis

Jiahui Chen·Yongjia Xu·Xin Yang·Zixuan Cang·Weihua Geng·Guo-Wei Wei
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Abstract

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.

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Jiahui Chen, Yongjia Xu, Xin Yang, Zixuan Cang, Weihua Geng, Guo-Wei Wei. 2023-11-29. Poisson-Boltzmann based machine learning (PBML) model for electrostatic analysis. https://arxiv.org/abs/2312.11482

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