Accelerating SUStainable TECHnological trajectories with computational chemistry and machine learning

Objective

SUSTECH addresses a critical paradox at the heart of modern society: synthetic chemicals have been instrumental in societal advancement in their manifold uses ranging from materials to fertilizers and pharmaceuticals. Yet, they pose escalating risks to human health and environmental sustainability. There is an urgent need to reorient technological development toward safer and more sustainable alternatives. This project aims to generate a comprehensive understanding of the technological pathways that lead to the commercialization of hazardous compounds to inform policy-makers how to redirect innovation in ways that align with societal and environmental well-being. SUSTECH investigates the decision-making processes and incentive structures that drive the development and the market approval of hazardous compounds to identify potentially misguided incentives. The interdisciplinary project integrates methodologies from computational chemistry and machine learning, with frameworks rooted in the economics of innovation. SUSTECH forecasts various characteristics, including toxicity profiles, of molecular structures disclosed in archival sources such as patents, linking these to related innovation activities and underlying incentives. This approach offers a robust foundation for evidence-based policy interventions. Moreover, SUSTECH will craft predictive tools for early hazard assessment in the innovation process, with key data and algorithms accessible via an open platform. The potential impact is immense: informing regulatory approaches to steer early-stage innovation towards sustainability. SUSTECH represents a paradigm shift, applying predictive chemistry methods to address this challenge. The PIs are leaders with complementary expertise: predictive algorithms using molecular data (OI), innovation studies and patent analyses in chemistry (AM) and pharmaceuticals (SW), and sustainability and science & technology policy (EG).