It is important to note, however, that integrating these ML models into CASP tools would require additional information about the individual reaction steps. This includes information about the reaction condition and the reaction mechanism. Information that is usually available in the underlying data of the CASP tools, but typically not provided. For example, a factor like pH can affect the protonation state of a molecule, which can be crucial for the reaction to proceed and have a great impact on the nucleophilicity and electrophilicity estimates. To address this, the MCA and MAA models would have to be accompanied by a pKa predictor to accurately determine the correct protonation state under the given reaction conditions. Alternatively, the CASP tools would need to provide the reactants in their actual protonation state instead of the neutral canonical form. Information about the reaction mechanism is another important factor for evaluating the chemical selectivity and determining the properties that drive the reaction. For example, if the reaction involves a catalyst, a single proposed reaction step could involve multiple key steps that influence selectivity such as the formation of a catalyst-substrate complex to unlock the true reaction site. Furthermore, knowledge about leaving groups is also important, as this can significantly impact the predicted MCA and MAA values. Unfortunately, the current CASP tools typically lack this level of reaction details and mechanistic insights. However, novel approaches for determining reaction mechanisms using ML have recently emerged, 42 which could be a step in the right direction for making CASP tools truly selectivity-aware through the use of MCA and MAA values. In terms of reactivity predictions, we show that the ML-predicted MAA values can be used to estimate the hydrolysis half-lives or rates of esters and carbamates to guide the chemical design toward more stable compounds.