Essential to Equation 1 is that k 20 • C can span from non-observable reactions (k 20 • C < 10 -5 M -1 s -1 ) to diffusion-controlled reactions (k 20 • C > 10 9 M -1 s -1 ), and its applicability has been confirmed for various molecules resulting in Mayr's database that currently holds experimental reactivity parameters for 352 electrophiles and 1,261 nucleophiles. However, measuring reaction rates to extract reactivity parameters is generally time-consuming and often difficult at the extremes of the reactivity scales. Thus, several in silico methods have been proposed to circumvent this problem. This includes estimating the rate constant using the Eyring equation and computing the reactivity parameters from frontier molecular orbital (FMO) energies or chemical affinities . Furthermore, a lot of new ML approaches based on Mayr's database have recently emerged. In this work, we will focus on the studies by Van Vranken and Baldi showing that calculated methyl cation affinities (MCAs) and methyl anion affinities (MAAs) of structurally different molecules correlate with Mayr's N •s N and E, respectively, when considering solvent effects. Based on these findings, they created two QM-derived datasets with reactivity parameters for 1,232 nucleophiles and 1,113 electrophiles (we have excluded 76 duplicates) covering ∼ 50 orders of magnitude in each case. The datasets were used to train different ML models by treating the affinities as either atomic, functional group, or molecular properties. The best-performing architecture was an atom-based graph attention network (GAT) achieving 10-fold cross-validation R 2 coefficients of 0.92 ± 0.02 and 0.94 ± 0.02 for MCAs and MAAs, respectively. However, it is worth noting that the two datasets contain molecules that are not in Mayr's database, and the ML models have not been validated against experimental reactivity parameters. Furthermore, the atom sites are identified by hand, which makes it difficult to apply their method to arbitrary molecules in an automated fashion. This work will address these limitations by introducing a fast and fully automated quantum chemistry-based workflow that detects relevant sites and calculates associated MCAs and MAAs. In addition, we will apply the workflow to different tasks to highlight the applicability of MCAs and MAAs as quantitative measures of chemical reactivity.