Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset.

Physics-based approaches rely on accurate force fields and efficient sampling to provide mechanistic insight into biomolecular systems. Recent AI-driven advances are transforming this landscape, replacing empirically fitted force fields-built from manually curated atom types-with machine-learned models. Achieving interoperability between these new force fields and established sampling strategies-and validating them on challenging benchmark sets that extend far beyond near-native states-is essential for progress in the field. To this end, we introduce the FoldBind benchmark set, a collection of 18 systems encompassing 14 protein-folding cases and 4 peptide-protein complexes that undergo folding upon binding. This suite expands existing validation efforts by probing both conformational transitions and binding-induced folding, offering a rigorous test for sampling methods and force-field accuracy alike. To explore these systems, we employ the Modeling Employing Limited Data (MELD) framework as the sampling engine. MELD accelerates conformational exploration by integrating ambiguous or noisy physical restraints-for example, the general expectation that proteins form hydrophobic cores-within a Bayesian inference formalism. By balancing exploration (broad conformational search) and exploitation (stabilization of structures consistent with physics and data), MELD efficiently accesses native-like states that are otherwise inaccessible to conventional molecular dynamics. Under identical data conditions, the quality of the force field determines which states are stabilized and whether the correct native basin emerges. Furthermore, the ability of a force field to consistently stabilize the native basin among multiple data-compatible states provides an additional measure of its physical realism. Together, this FoldBind benchmark, along with the information used in MELD, can be used to test and distinguish future force field development efforts.