We here set out to develop an efficient surrogate model for a challenging bimolecular reaction, HAT within proteins, without the need to evoke a DFT calculation for a given calculation of a reaction barrier. While the model does not reproduce the DFT results perfectly, its error is approaching the accuracy of the underlying target method. The authors of the functional of our choice, BMK, targeted an accuracy of 2 kcal/mol on energy barriers, and, depending on the benchmarks, BMK achieves an MAE of 0.8 kcal/mol to 5 kcal/mol relative to CCSD(T) calculations. A limitation of the depicted method is that the type of reaction one can predict is predefined. The model predicts hydrogen atom transfer only, while we can not rule out proton coupled electron transfer to play a role in this system. Further, tunneling effects are ignored, and all QM calculations are performed in the gas phase. However, these last two points are limitations of the dataset, not the underlying method. As soon as higher quality data is available, the network can utilize these by, e.g., transfer learning. We thus propose our GNN-based model as a starting point to build more refined models such as one taking quantum effects into account.