AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms

• URL: http://arxiv.org/abs/2607.03513v1
• Date: Fri, 03 Jul 2026 17:39:17 GMT
• Title: AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms
• Abstract summary: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model.<n> operating at this resolution allows for post-processing through force field energy evaluations and MD simulations.<n>We demonstrate the utility of this paradigm on absolute hydration free energy (AHFE), producing estimates 4-10x faster and with comparable accuracy to standard GPU-based MD.
• Score: 11.503551580974333
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD). This is in contrast with existing generative models that remove degrees of freedom by operating on coarse-grained, vacuum, or implicit solvent systems. Operating at this resolution allows for post-processing through force field energy evaluations and MD simulations, and enables the prediction of relevant properties in a gray-box manner (as ensemble averages of potential energy evaluations over generated samples). We demonstrate the utility of this paradigm on absolute hydration free energy (AHFE), producing estimates 4-10x faster and with comparable accuracy to standard GPU-based MD. By generating uncorrelated samples from alchemical Boltzmann distributions, we create more accurate, interpretable, and refinable ensemble predictions with calibrated uncertainty estimates, unlike regression methods which are entirely black-box predictors. Our approach also yields predictable benefits from increasing train- and test-time compute, realized by scaling model size and generating more samples, respectively. We believe that this approach demonstrates the utility of high-resolution ensemble generation for free energy estimation, with future potential to replace MD in tasks such as the prediction of lipophilicity, membrane permeability, or absolute binding free energy (ABFE) -- whose grounding and interpretability may be critical for the development of new drugs and materials.

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