A more recent model, 3DReact, was developed by van Gerwen et al. to predict reaction properties from 3D structures of reactants and products. The authors acknowledged the success of 2D-based models in predicting reaction properties and activation energy barriers, primarily due to their ability to map atoms between reactants and products. While 3D molecular representation models have been successful in predicting molecular properties, their application to reaction properties remains challenging. To address this, 3DReact integrates atom-mapped molecular graphs with 3D geometric information as priors, enabling more accurate predictions of energy barriers. The model was benchmarked using three reaction datasets; the GDB7-22-TS dataset used to train DimeNet++ in the previous work by Spiekermann et al. containing close to 12,000 diverse organic reactions, the Cyclo-23-TS dataset of 5269 reaction profiles for [3+2] cycloaddition reactions 139 with activation free energy barriers calculated at the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory, and the Proparg-21-TS dataset of 753 structures involved in the enantioselective TS of benzaldehyde propargylation, with activation energies computed using B97D/TZV(2p,2d).