Up to this point, the reviewed literature has focussed on approaches akin to double-ended TS searches, where 3D structures of reactants and products serve as inputs to predict a 3D TS geometry. While some of these methods demonstrate a commendable level of accuracy on withheld test data, a significant limitation is their dependence on precise reactant and product structures. For optimal accuracy, these structures must be identified in their lowest energy conformations and optimised using DFT. Although optimising equilibrium reactant and product structures is generally faster than optimising the corresponding TS, it remains computationally intensive. Additionally, training these models requires accurate databases of optimised reactants, products and their corresponding TSs, which can be challenging to compile, especially for complex or less well-characterised reaction classes. To circumvent these limitations, several methods that do not rely on 3D structural information as inputs have been proposed.