In the context of computational asymmetric catalysis, an overarching goal of the computational methods is the prediction of enantioselectivity of asymmetric reactions, enabling computational reaction optimization (i.e., optimal catalysts, substrates, ligands, and conditions). Different methodologies for achieving these goals exist, and they can be divided in two main categories: 1) methods requiring sufficient knowledge on the reaction mechanism (primarily QM and MM-based methods) and 2) data-driven methods (primarily databases and ML methods). Irrespective of category, the overall principles are similar and involve data collection, identification of meaningful patterns, parameters or features, model building and testing, followed by model refinement (Figure ).