Predictive models were evaluated through varying parameter choices to determine features with strong contributions to the biodegradability target. Since the input molecules with biodegradability values come from a single set, a 80-20 train-test split was performed and for the classification task, metrics for overall accuracy and each class (see Supplementary tables) were obtained ('0' for non-biodegradable and '1' for biodegradable). During training, 5-fold cross validation was used to construct the average of several hold-out 'validation' samples, while the final unseen test set is used to compute the final metrics. These approaches have been adopted from comparable QSAR models with the J-MITI dataset . The accuracy, f1 metric, and various weighted accuracies were used as either alternate metrics or the primary metric to optimize the model, as are the specificity/sensitivity for classification tasks.