Among the 3D input methods, models were categorised into those predicting TS distance/Coulomb matrices and those employing SE(3)-equivariance to directly predict Cartesian coordinates. Several ML architectures, such as TFNs, GANs, and diffusion models, were explored. The review concluded with a comparison of these models, noting that architectures currently represent the state-of-the-art in both 3D and 2D graph-based methods. In particular, the OA-ReactDiff+rec model is starting to approach chemical accuracy in its predictions of activation energy barriers. Additionally, a recent RL model based on the actor-critic framework demonstrated exceptionally low RMSDs and barrier MAEs, though only for a limited number of small test cases.