In this work, we introduced a workflow to train machine learning models for fast predictions of energy barriers of hydrogen atom transfer reactions, spanning a wide range of more than 80 kcal/mol and covering the heterogeneous chemical space of a protein. Our model was trained and evaluated in the context of radical migration in collagen fibrils, but can be transferred to other chemical systems subject to HAT reactions. Since the predicted reaction barriers are based on 3D structures of molecules, without any DFT optimization or energy valuation, the model can be used in direct conjunction with MD simulations. For example, utilizing predicted barriers in a kinetic Monte-Carlo scheme, one can extend MD simulation to allow HAT reactions to take place in a dynamically evolving molecular system. Our study emphasizes the strength of graph neural networks for predicting chemical reactivity -even in such challenging cases as dynamic biopolymers.