The potential of the ML models is demonstrated through their accurate predictions of both chemical selectivity and reactivity. For example, we show that the ML models can successfully predict chemical selectivity in cases that are typically challenging for data-driven computer-aided synthesis planning (CASP) tools to learn. As a result, we propose to integrate these ML models into CASP tools to flag reaction steps that can lead to low yields or undesired products. The proposed synthetic routes can then be ranked based on the number of flags to ensure the selection of the most reliable synthetic pathways.