In comparison to data-extensive machine learning methods, previous research has centered on the importance of chemical descriptors, such as those in the PubChem property database , in predicting properties such as biodegradability. Katritzky et al 3 describe the use of logP and charge information to predict properties such as electrostatic potential, molecular surface area, and molecular volume. Taken in combination, the identification of various collinearities between sets of predictors, such as between physicochemical and molecular fragment descriptors, allows us to draw further qualitative relationships (visualized in Figure ). While we originally considered these set of PubChem property descriptors, we found that several of them could be well-predicted by the GCM and 3D descriptors. For this purpose, regression models were run against various physicochemical features and found certain correlations with the model features (Figure ). Octanal-water partition can be reliably predicted with molecular fragments, as are the strongest descriptors in the physicochemical model (cation, anion and ring counts). The most identified cations were *=N-* and c[NH2] (imines, hydrazines) while the most identified anions were *-C(=O)-*, N=* and *-S=O (carbonyls/carboxylic acids, amines/amides, sulfonyls), suggesting that electronegativity plays a role in charged tendency. Since cations and anions contribute negatively and positively to biodegradability respectively, this supports degradation having an oxidative mechanism . We also observed a correlation of ring quantity with aromatic-specific substructures and biodegradability, confirming the contribution of aromatic degradation mechanisms, even though benzene structures remain highly resistant.