用深度学习加速生物分子静电能计算，100倍提速且精度不降。
End-to-End Reaction Field Energy Modeling via Deep Learning based Voxel-to-voxel Transform
- 将电势方程输入转为体素表示，用神经场变换器求解。
- 相比传统方法提速超100倍，精度接近广义玻恩模型。
- 适合需要快速模拟分子静电作用的研究者使用。
在计算生物化学与生物物理中，理解静电相互作用对揭示生物分子的结构、动态与功能至关重要。泊松-玻尔兹曼（Poisson-Boltzmann, PB）方程是建模这些相互作用的基础工具，用于描述带电分子内部及周围的静电势。然而，由于生物分子表面复杂且需考虑移动离子，求解PB方程面临巨大计算挑战。传统数值方法虽准确但计算成本高，且随系统规模增大而急剧恶化。为此，我们提出PBNeF，一种受神经网络求解偏微分方程最新进展启发的机器学习新方法。该方法将PB方程的输入与边界条件转化为可学习的体素表示，利用神经场变换器预测PB解及反应场势能。大量实验表明，PBNeF相较传统PB求解器实现超过100倍的速度提升，同时保持与广义玻恩（Generalized Born, GB）模型相当的精度。
原文摘要 · Abstract (English)
In computational biochemistry and biophysics, understanding the role of electrostatic interactions is crucial for elucidating the structure, dynamics, and function of biomolecules. The Poisson-Boltzmann (PB) equation is a foundational tool for modeling these interactions by describing the electrostatic potential in and around charged molecules. However, solving the PB equation presents significant computational challenges due to the complexity of biomolecular surfaces and the need to account for mobile ions. While traditional numerical methods for solving the PB equation are accurate, they are computationally expensive and scale poorly with increasing system size. To address these challenges, we introduce PBNeF, a novel machine learning approach inspired by recent advancements in neural network-based partial differential equation solvers. Our method formulates the input and boundary electrostatic conditions of the PB equation into a learnable voxel representation, enabling the use of a neural field transformer to predict the PB solution and, subsequently, the reaction field potential energy. Extensive experiments demonstrate that PBNeF achieves over a 100-fold speedup compared to traditional PB solvers, while maintaining accuracy comparable to the Generalized Born (GB) model.
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