Another application of the presented ML models is to determine chemical stability by applying the MAA values to predict hydrolysis half-lives or rates of esters and carbamates. Figures and show the correlation between MAA values and experimental hydrolysis halflives and rates of esters and carbamates under neutral and basic conditions, respectively. The MAA values are obtained for the neutral species without adjusting the protonation state to the reaction conditions, and only the maximum MAA value is used if multiple ester and carbamate groups are present in the same molecule. The results in Figure show the correlation between QM-calculated and ML-predicted MAA values against experimental hydrolysis half-lives of 29 esters and carbamates under neutral pH. Unfortunately, 15 of the QM calculations resulted in connectivity changes during the geometry optimizations as indicated by the red circles in Figure . The reasons for the connectivity issues include hydrogen transfers, cyclization, and fragmentation of the starting structure. Consequently, the MAA values are generally too high for the MAA calculations with connectivity issues, and these results are therefore excluded from the analysis as seen in Figure . In contrast, the ML model does not encounter this problem as the molecular geometries are optimized using MMFF. The Pearson correlation coefficient for all 29 compounds using the ML model is -0.87 compared to the QM-calculated MAA values with a Pearson correlation coefficient of -0.82 for the 14 compounds without connectivity issues. As seen in Figure , two of the ML-predicted MAA values had an estimated error above the predefined 30 kJ/mol cutoff. However, both of these data points are in good agreement with the regression line.