RDKit is used to sanitize molecules; 21 ones which have incorrect or unrecognized SMILES are discarded. Templates are extracted using the RDChiral package, and specific logic is used to target ring-forming reactions by augmenting the atoms involved in the reaction with the atoms in the formed ring, along with their respective heteroatoms. The extraction step generates a unique identifier per template, based on its respective fingerprint. Once the templates are extracted and validated, the second pipeline is utilized for expansion policy preprocessing and training. The template dataset and a 'lookup' dataset, which locates examples of the generated templates in databases, are split into training, validation, and test sets. The model is trained using a Keras model built in Tensorflow. A RingBreaker model is run in parallel to the expansion model in the modelling pipeline. The expansion policy is combined with MCTS to sift through the search space.