用神经网络提升蛋白质溶剂化模型精度，且跨蛋白系统仍有效。
All-atomistic Transferable Neural Potentials for Protein Solvation
- 基于物理先验设计数据高效神经势能，学习可迁移的修正参数。
- 相比传统方法，溶剂化能预测更准确，跨域蛋白系统表现稳定。
- 适合需要高精度溶剂化计算的药物分子模拟研究者。
隐式溶剂模型通过减少溶剂自由度来计算溶剂化能量，但精度常低于显式模型。近年来神经势能在药物发现中展现潜力，但可迁移性仍是挑战。本文提出蛋白质水合神经网络（PHNN），一种基于解析连续介质溶剂化的隐式模型，通过学习可迁移的参数修正而非事后调整能量。该模型显式设计以最大化数据效率，利用数据中的物理先验。结果表明，PHNN在精度上优于传统解析方法，并在跨域蛋白系统上保持良好预测能力。
原文摘要 · Abstract (English)
Implicit solvent models are widely used to decrease the number of solvent degrees of freedom and enable the calculation of solvation energetics without water molecules. However, its accuracy often falls short compared to explicit models. Recent advancements in neural potentials have shown promise in drug discovery, but transferability remains a persistent challenge. Here, we introduce the Protein Hydration Neural Network (PHNN), an implicit solvent model that extends analytical continuum solvation by learning transferable corrections to model parameters instead of applying post hoc adjustments to final energies. The model is explicitly designed to maximize data efficiency by leveraging physical priors embedded in the data. We demonstrate that PHNN improves accuracy relative to traditional analytical methods and maintains predictive accuracy on out-of-domain protein systems.
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