Expanding the applicability of these models to more general chemistry, such as in pharmaceutical synthesis, will require efforts to adapt existing methods and architectures or employ transfer learning strategies to understand more complex and diverse chemical spaces. Additionally, while some models are approaching the limits of chemical accuracy with calculated activation energies, there is still room for improvement. Only a limited number of the studies have explored the use of predicted TS geometries for calculating chemically relevant properties such as barriers, either through direct single-point energy calculations and comparison to their corresponding reactants, or by integrating predicted geometries into downstream ML property predictors, and the effectiveness of these approaches remains underexplored. Despite recent advancements, many test cases still require costly DFT optimisations to achieve the desired accuracy, a limitation that will likely worsen as models are extended to larger and more complex chemical systems.