Our machine learning model predicts the energy barrier for one selected reaction at a time while taking the chemical environment around the radical in the reactant state as input.(Figure ) Importantly, the reactant state is directly cut out from a molecular dynamics (MD) simulation, and no optimization is needed prior to the barrier prediction, neither a single step energy calculation as input. It thus can be used as a surrogate model of hydrogen atom transfer within classical MD simulations to model radical propagation within collagen or other (bio)materials on the fly, e.g., by using hybrid kinetic Monte-Carlo and MD simulations. Our GNN-based approach tackles the challenge of predicting reaction barriers in a heterogeneous and dynamic chemical setting, and will likely prove useful for other complex soft matter systems.