Knowledge Distillation of a Protein Language Model Yields a Foundational Implicit Solvent Model.

Implicit solvent models (ISMs) promise to deliver the accuracy of explicit solvent simulations at a fraction of their computational cost. However, despite decades of development, their accuracy has remained insufficient for many critical applications, particularly for simulating protein folding and the behavior of intrinsically disordered proteins. Developing a transferable, data-driven ISM that overcomes the limitations of traditional analytical formulas remains a central challenge in computational chemistry. Here, we address this challenge by introducing a novel strategy that distills the evolutionary information learned by a protein language model, ESM3, into a computationally efficient graph neural network (GNN). We show that this GNN potential, trained on effective energies from ESM3, is robust enough to drive stable, long time-scale molecular dynamics simulations. When combined with a standard electrostatics term, our hybrid model accurately reproduces protein folding free energy landscapes and predicts the structural ensembles of intrinsically disordered proteins. This approach yields a single, unified model that can be transferred across both folded and disordered protein states, resolving a long-standing limitation of conventional ISMs. By successfully distilling evolutionary knowledge into a physical potential, our work delivers a foundational ISM poised to accelerate the development of predictive large-scale simulation tools.