The first example of ML applied to the prediction of TS geometries was reported by Pattanaik et al. in 2020 which utilised a GNN for predicting the distance matrix of the TS. Trained using the Isomerisation dataset, graph representations were first generated for the DFT reactant and product geometries. These reactant and product graphs were averaged to create a guess structure of the TS and this was subsequently input into the GNN where the representation of the TS was updated. Based on this representation, the distance matrix of the TS geometry was predicted, along with a separate matrix containing weights to be used in nonlinear least squares (NLS) optimisation. The NLS optimisation was used to convert the predicted TS distance matrix back to Cartesian coordinates, to obtain the final predicted TS geometry. The predicted TSs were verified using a quantum chemistry workflow, which involved optimising the TS using DFT and performing an internal reaction coordinate (IRC) calculation to confirm that the TS corresponded to the correct reactant and product. Out of the 834 reactions tested, 71 % of the predicted TSs were successfully optimised to the correct structure, with a test distance mean absolute error (D-MAE) of 0.28 Å, while the remaining 29 % of TSs, which did not optimise successfully, had a higher test D-MAE of 0.43 Å.