ArticleBiophysical journal2024
Poisson-Boltzmann-based machine learning model for electrostatic analysis.
Article in Biophysical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- AmberTorchPB: A Unified Framework for Poisson-Boltzmann-Based Reaction Field Energy Calculation via Tensor Computation.Journal of chemical theory and computation · 2026Article
- A DNN Biophysics Model with Topological and Electrostatic Features.Journal of chemical information and modeling · 2026Article
- Article
- End-to-End Modeling of Reaction Field Energy Using Data-Driven Geometric Graph Neural Networks.Journal of chemical theory and computation · 2025Article
- Implicit Solvent Models and Their Applications in Biophysics.Biomolecules · 2025Review
- Machine learning tools advance biophysics.Biophysical journal · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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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Registered trials
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