In recent years, computer-assisted synthesis has gained significant momentum and several computational methods (most commonly machine learning (ML) methods and statistical models) applied to organic synthesis problems have been reported. For example, computeraided synthesis planning has advanced rapidly and can propose reaction mapping and realistic retrosynthesis (e.g., Chematica/Synthia and AIZynthFinder ), can predict yields, catalyst inhibition, regioselectivity, and chemical reactivity. In practice, these predictive trained methods have been successfully used to design new catalysts and predict the stereochemical outcome of asymmetric reactions. For the latter, ML techniques are advantageous over QM and molecular mechanics (MM) for two reasons: speed (orders of magnitude faster than QM) and their application to reactions with unclear or complex mechanisms (ML models developed from catalyst structures only). However, training ML models requires a significant amount of experimental data for training and can hardly be applied to new reactions, as emphasized by Norrby in a viewpoint. Reaction/chemical mapping is more general; for example, physicochemical and QM descriptors of over 300,000 monodentate phosphine ligands have been added to a database named Kraken, which will certainly be particularly useful for new reactions relying on phosphine ligands. However, despite all these successful developments, much is left to be done in the field, including the use of these methods in prospective studies.