More recently, scores based on ML have emerged. SCScore is based on the assumption that the reactants of a reaction should be easier to make than the products, and trains a model for such ranking, which can then be applied to score new molecules . Such scores can also be used to guide synthesis planning algorithms . SYBA is a binary classifier trained to distinguish between real and algorithmically generated, supposedly hard-to-synthesize molecules . More recently, Liu et al and Thakkar et al proposed models to approximate the output of synthesis planning algorithms . They first perform synthesis planning to compute routes for a large test set of molecules, and train the models to predict whether a route for the molecule could be found, or predict scores derived from the routes, such as the number of steps, or cost.