The growth of synthetic chemicals comes with the issue of managing their waste-producing and environmental impacts. A considerable number of synthetic materials are non-biodegradable, resistant to the actions of chemical and biological degradation processes, persisting in the natural environment for long periods of time. The identification of readily biodegradable (RB) substitutes for common polymers would hence be a critical development . To speed up the identification of biodegradable materials and their substitutes, filtering of large sets for high-throughput molecular discovery is an attractive option. Chemical prediction with quantitative structure-activity relationships (QSAR) uses descriptor-based statistical analysis to derive relationships, and have been used to identify molecular features contributing to the octanolwater partition (logP) and solubility. Unlike physicochemical properties with relatively consistent pathways and activating structures, properties such as bioavailability and toxicity are dependent on a combination of molecular mechanisms. Thus, a small group of descriptors is not sufficient to characterise such properties. A concept of note in the field is the applicability domain (AD) , where the predictions of a model fall into a threshold of certain related compounds.