1373 SMILES string inputs from J-MITI together with their associated biodegradability data were processed, though these inputs notably lacked stereochemistry, compounds. The J-MITI biodegradability data contains different duration schemes for each SMILES. Hence, a subset of these molecules is used for classification according to one duration. Since duration schemes longer than a selected scheme that fall above the degradability threshold can be taken to fall above the threshold for the selected scheme, the molecules selected can be expanded to include such schemes as well. Thereafter, certain conformers were excluded at random to achieve class balance between non-biodegradable and biodegradable compounds in the models. Conformers for each molecule were generated at two levels: the low-level MMFF94 optimization in RDKit , and semi-empirical calculations using the program of Conformer-Rotamer Ensemble Sampling Tool (CREST) (only 1117 SMILES strings were successfully exported to CREST conformers). Root means square (RMS) similarity with alignment was used to cluster up to 50 conformers per molecule, a number sufficient to incorporate the variety in conformational extensivness of the molecules studied . A representative conformer from each cluster was used to generate fingerprints, 3D-derived descriptors and other descriptors for the dataset (X) (for the purposes of machine learning, these descriptors shall be referred to as 'features'). This is to be mapped to a target (Y), the primary variable studied here being raw biochemical oxygen demand.