Various approaches have been reported for feature design in machine learning applications within the chemical literature, which can broadly be separated into two categories: generalized strategies encoding as many physical properties of molecules as possible, and hand-picked approaches that select relevant features that support a mechanistic hypothesis. In the former case, feature generation has seen extensive development, including the Coulomb Matrix, Bag of Bonds, crystal structure representation, and fingerprinting methods. While these approaches have been shown to be suitable for a range of broad machine learning problems in chemistry without clear or obvious structure-output relationships, we chose the second class of feature design due to our clear hypotheses for relevant features to use to solve our learning tasks. In particular, we sought out electronic and structural features to encode information about the charge density and bond structure of candidate carbon atom O2-binding sites. These include the atomic charge differences in the neutral and cationic states molecule, the unpaired spin density, and properties of related to aromaticity. For the full list of features prepared for the machine learning models developed in this work, see SI section 2.