Having a comprehensive data set of BDEs at hand, we analyzed trends in the BDEs across the chemical space covered by the amino acids. We grouped the X-H BDEs according to the reference compound used in the isodesmic reaction and show the distribution of the ten largest groups as well as an example radical for each group in Figure . Since the reference compounds were chosen to correspond to the local chemical environment of the radical, Figure shows the influence of the local chemical environment on the BDEs. The observed stability trends agree with known chemical principles: Captodative and resonance stabilization leads to low BDEs, as can be seen for C α -H, benzylic C-H and O-H bonds. Secondary C-H bonds and C-H bonds adjacent to a heteroatom have medium high BDEs. Primary C-H BDEs are higher. N-H, O-H, and aromatic C-H bonds are associated with the highest BDEs. Finally, we sought to evaluate the utility of our data set in the context of machine learning. To this end, we compared our data set to predictions made by ALFABET, a graph neural network for the prediction of BDEs trained on small organic molecules consisting of up to ten heavy atoms. Considering that most dipeptides in our data set consist of more than 10 heavy atoms, ALFABET predictions agree reasonably well with BMK BDEs (R 2 = 0.918, MAD = 11.3 kJ/mol, ??). There is, however, considerable scatter for lower BDEs, suggesting that ALFABET does not fully capture the chemistry of amino acid radicals. Our data set could therefore prove useful in a transfer learning setting for the radical chemistry of proteins.