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

2026-07-03

AI Summary

This work addresses the high computational cost of conventional molecular dynamics simulations and the limited accuracy of existing generative models, which often rely on coarse-grained representations or implicit solvent approximations that hinder precise free energy predictions. The authors propose the first deep generative model capable of operating with full-atom resolution, explicit solvent, and periodic boundary conditions, enabling efficient direct sampling of molecular configurations from the Boltzmann distribution. By integrating physical constraints into the generative framework, the method yields interpretable and refinable ensemble predictions while providing well-calibrated uncertainty estimates. In absolute hydration free energy prediction tasks, the model achieves accuracy comparable to GPU-accelerated molecular dynamics but with a 4–10× speedup, demonstrating strong scalability.

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.

Problem

• molecular dynamics
• generative models
• Boltzmann distribution
• free energy estimation
• all-atom simulation

Innovation

• generative modeling
• molecular dynamics
• Boltzmann distribution
• explicit solvent
• free energy estimation