All-atomistic Transferable Neural Potentials for Protein Solvation
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 ph...
| Authors | Rishabh Dey, Salvina Sharipova, Konstantin Popov |
|---|---|
| Primary category | physics.chem-ph |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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