with R denoting the set of predicted reactants. The advantage of such reformulation is realised through its powerful decomposition. Particularly, the authors introduced a template applicability check within the model. The check ensures that a template t can only be assigned a probability P (R|T , O) > 0 if the product subgraph o T is found within product O. Thus, only selected templates are assigned a positive probability. This is in stark contrast with Neuralysm, for which all templates are assigned a probability P (T |O) > 0. Concurrently, Seidl et al. recognised that models disregard structural similarities found within templates e.g. two templates that only differ in one halogen functionalisation might be assigned largely different probabilities. They introduced a modern Hopfield network (MHN) to learn the similarities between the structure of molecules and templates. Their model (MHNReact) thus "retrieves" the best template from the template library. MHNReact was seen to work particularly well for few/zero-shot templates i.e., templates that seldom appear in the training database.