A major challenge in predicting chemical reactivity in proteins, such as unspecific radical transfer reactions, is the molecular environment of the reaction: it determines the reactivity but is both chemically very diverse and highly dynamic. This leads to a virtually infinite number of possible reaction scenarios, in which reactants represent instances within a vast chemical and conformational space. As a consequence, directly computing this amount of radical reactions by ab initio calculations is computationally not feasible. Instead, machine learning can leverage quantum chemical calculations by predicting reactivity based on an initial quantum chemical data set. We here set out to predict the energy barriers of hydrogen atom transfer reactions in proteins using Graph Neural Networks that are trained on computed energy barriers.