All-atomistic Transferable Neural Potentials for Protein Solvation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918501533351936 |
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| author | Dey, Rishabh Sharipova, Salvina Popov, Konstantin |
| author_facet | Dey, Rishabh Sharipova, Salvina Popov, Konstantin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14584 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | All-atomistic Transferable Neural Potentials for Protein Solvation Dey, Rishabh Sharipova, Salvina Popov, Konstantin Chemical Physics Machine Learning 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. |
| title | All-atomistic Transferable Neural Potentials for Protein Solvation |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2605.14584 |