In this paper, various cheminformatics approaches were used in combination to understand the chemical space of biodegradable compounds. Here it is shown that 3D conformer-based descriptors contribute meaningfully to predictive accuracy despite the reservations of earlier models. The use of 3D descriptors was effective in not only improving the understanding of molecular geometry but also in augmenting the training data, with multiple conformers producing an educated guess on the mean 3D properties of each molecule. Because of the referenceinvariant properties of the descriptors, the random generation of conformers will not have any effect on their interpretation. In addition, these descriptors have appropriate sensitivity to produce a meaningful set of values for each molecule without much noise. For future work, we note that both group contribution methods and connectivity indices have been used to predict desired properties of interest in compounds and materials using only a fragment of the structure . In the features presented, measures of molecular shape are more appropriate for monomeric molecules. Oligomers, polymers and other molecules consisting of repeated sections might have biodegradability trends dependent on the fragments formed after their first dissociation, which is normally more spontaneous. Taking this further, some models perform an unsupervised learning pipeline across the molecular space to identify subgroups of compounds, of which test compounds can then be assessed based on their suitability for a certain AD. Extended connectivity fingerprints exploit the graph structures to encode substructures of chosen scale into discrete bit vectors. We have looked into the use of graph-based MPNNs supplemented with RDKit and MORSE features. Besides studies based on polymers 27 , Mohapatra 20 also utilized similar GNN-baed methods to construct representations of macromolecules, with a focus on structural similarity. These were applied to glycans and AMPs where unsupervised dimensionality reduction and supervised classification were applied.