In the second application, we show that the workflow can be used to predict the reactivity of covalent inhibitors. We report a strong correlation between MAAs and calculated activation energies for various acrylamides and propargylamides similar to the work by Hermann et al. using a warhead-associated electrophilicity index. The results of the 2-chloroacetamides showed no correlation for both the warhead-associated electrophilicity index and MAAs, which could be due to errors in the calculated activation energies. The advantage of the MAAs over the warhead-associated electrophilicity index is that the MAAs are atom-specific and completely straightforward to calculate. Whereas, the warhead-associated electrophilicity index requires a selection of the highest occupied and lowest unoccupied orbitals that are associated with the warhead to match the performance of the MAAs. Future work will use the QM-based workflow to calculate MCAs and MAAs for a large set of diverse molecules and train an atom-based ML model for each property similar to the one presented in Ree et al. . The ML models will then be used to predict the chemical selectivity for a large set of experimentally reported reactions to provide a statical basis for using the MCAs and MAAs to predict chemical selectivity. An example of the visual output can be seen in Figure .