Dynamics-aware-FeatureDock

Structure-based machine learning has ridden one wave: AlphaFold made static structure cheap and universal, and models like FeatureDock, Boltz-2, and DiffDock-L now predict binding from a single frozen pose. But proteins are not statues. Much of what determines whether a drug binds — and how tightly — lives in µs–ms conformational motion that no static structure captures. We believe dynamics is the next dimension, and the next leap in AI for protein design will come from models that can see it. We investigated whether ground-state dynamics information improves ML models of small-molecule–protein binding. Using Claude Science, we integrated Dyna-1, which predicts per-residue conformational exchange from any protein structure with no experiment required, into FeatureDock, adding a dynamics channel to its pocket features. As hypothesized, pose prediction improved most in flexible regions, exactly where a single static structure fails. This matters because it is a concrete step toward making dynamics a reusable, experiment-free ground-truth signal for machine learning. The same prior could sharpen co-folding and docking engines like Boltz-2 and DiffDock-L, and give protein-design pipelines a way to engineer motion on purpose, turning dynamics from an after-the-fact surprise into an optimizable design objective across countless AI-protein models.