Abstract: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.
| Subjects: | Chemical Physics (physics.chem-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14584 [physics.chem-ph] |
| (or arXiv:2605.14584v1 [physics.chem-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14584 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rishabh Dey [view email]
[v1]
Thu, 14 May 2026 08:54:41 UTC (3,668 KB)