In conclusion, our work showcases the range of models in chemical property prediction applied to the problem of biodegradability. Deep learning models construct features through direct input of molecular representations, with the potential to identify important fragments of compounds, and ongoing work is looking to bridge the gap between interpretable structure-activity models and machine learning techniques, towards molecule-specific mapping of atom and bond importances to a compound.