GNNs are particularly well-suited for modelling molecular structures due to the natural alignment with the graph-based representation of molecules. They capture local structural information by aggregating features from neighbouring nodes, which helps to represent the immediate chemical environment and bonding interactions effectively. Moreover, compared to more complex architectures, such as diffusion models or transformers, GNNs offer a relatively inexpensive and computationally efficient solution for predicting TSs. However, GNNs often struggle with capturing long-range dependencies, as local aggregation methods fail to adequately capture the spatial and electronic interactions between atoms that are several bonds apart, which can be critical when predicting TSs. For more information on NNs and GNNs, please refer to sources such as Ref. and Ref. , respectively.