First and foremost, f depends on the electrostatic interactions along the abstraction site -hydrogen -radical site point-charge array in the transition state (TS) geometry, which can be probed indirectly by considering atomic charges (in the reactant and product geometries). Additionally, f also depends on the sterics around the radical and abstraction sites -steric bulk around those sites can be expected to strain the transition state geometry and hence should result in a higher crossing-point between the reactant and product diabatic curves. Commonly, buried volume (V bur ) is used as a descriptor to quantify steric bulk around reactive sites. Finally, f has also been suggested to depend on the extent of delocalization in reactant and product, which can be probed through consideration of the spin density (ρ). However, an alternative viewpoint is that losses and/or gains in delocalization energy may already be fully reflected in the thermodynamic driving force term (∆E rp ), so that the impact of delocalization on f may actually be negligible. As indicated above, B depends on the interaction between the various diabatic states, and has been connected primarily to the (local) ionization potentials and electron affinities -and/or electron negativities -at the radical/abstraction sites. The discussion in the previous paragraphs provides us with an ideal QM descriptor representation that should render the mapping function from reaction to HAT barriers as linear as possible. As already indicated in the introduction, computing this complete input representation would be complex and expensive -many different DFT calculations would be needed (for both charged and uncharged species) for every data point. At the same time, the literature suggests that even a subset of the descriptors mentioned above could be sufficient to build an informative representation: for small subsets of the chemical space we are interested in, various groups have previously constructed effective multivariate linear models, as well as some more advanced architectures (random forests, neural networks, etc.) with a subset of the descriptors listed above. As already indicated above, encouraged by these findings, we selected the BDE-db dataset as a suitable source of descriptors. This dataset contains (relaxed) BDFEs -at room temperature in the gasphase -and BDEs, (Mulliken) atomic charges, and (Mulliken) spin densities for over 200,000 organic radical compounds. Additionally, since accurate geometries are included in this dataset, we were able to expand this descriptor list with buried volume values at a marginal computational cost. Finally, we also used these geometries to rapidly compute vertical, i.e., frozen or unrelaxed, BDE values at extended tight-binding (xTB) level of theory (these final low-level descriptors were found to be remarkably predictive -even at this low theory level -for activation energies of HAT reactions between P450 enzymes and organic compounds in a recent study by Gingrich et al. -see the Methodology section for more details).