Industrial ecology has revolutionized our understanding of material stocks and flows in our economy and society. For this important discipline to have even deeper impact, we must understand the inherent nature of these materials in terms of human health and the environment. This paper focuses on methods to design synthetic chemicals to reduce their intrinsic ability to cause adverse consequence to the biosphere. Advances in the fields of computational chemistry and molecular toxicology in recent decades allow the development of predictive models that inform the design of molecules with reduced potential to be toxic to humans or the environment. The approach presented herein builds on the important work in quantitative structure–activity relationships by linking toxicological and chemical mechanistic insights to the identification of critical physical–chemical properties needed to be modified. This in silico approach yields design guidelines using boundary values for physiochemical properties. Acute aquatic toxicity serves as a model endpoint in this study. Defining value ranges for properties related to bioavailability and reactivity eliminates 99% of the chemicals in the highest concern for acute aquatic toxicity category. This approach and its future implementations are expected to yield very powerful tools for life cycle assessment practitioners and molecular designers that allow rapid assessment of multiple environmental and human health endpoints and inform modifications to minimize hazard.
Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods
