Our ultimate aim is to model the chemistry of radical-induced damage to collagen, whilst simultaneously capturing the dynamic nature of this system. Kinetic Monte-Carlo (KMC) method enables incorporating reactions into MD on timescales beyond those covered by conventional MD simulations. A hybrid KMC-MD approach models reactions in a Markov-process, allowing arbitrarily big time jumps between reaction steps. Previously, we coupled KMC with MD to simulate homolytic bond rupture in stretched collagen fibrils in a method called KIMMDY. Our GNN-based model for predicting reaction barriers allows applying the KIM-MDY approach to radical transfer reactions. Inferring an energy barrier of a reaction from a trained neural network within a reactive MD simulation substitutes the otherwise computationally costly quantum chemical calculation, and only marginally compromises the efficiency of standard MD simulations. In the past, several methodologies were developed to achieve reactive MD, including reactive force fields, such as ReaxFF and AIREBO , hybrid quantum mechanical / molecular mechanical (QM/MM) simulations, and, more recently, molecular dynamics simulations paired with machine-learned force fields (MLFF). However, all these methods are slower compared to regular MD and are by default restricted to reactions on the timescale of the simulation. KIMMDY overcomes these drawbacks but relies on the availability of reaction rates, which can now be provided with the model introduced here. Implementation and results of the extended KIMMDY software are beyond the scope of this paper, and will be published later.