Toxic-free products: Europe funds the SUSTECH project with an ERC Synergy Grant worth approximately €10 million

We are surrounded by chemicals that are potentially hazardous to health and the environment, in food, cosmetics, household products, pesticides, medicines and even fabrics. Understanding how and why this happens and preventing it is the goal of SUSTECH – Accelerating SUStainable TECHnological trajectories with computational chemistry and machine learning. The project has just been awarded an ERC Synergy Grant 2025, one of the most prestigious and competitive grants in Europe, worth approximately 10 million euros. The winners are Elisa Giuliani from the University of Pisa, Arianna Martinelli from the Sant'Anna School of Advanced Studies in Pisa, Stefan Wagner from the University of Vienna and Olexandr Isayev from Carnegie Mellon University in Pittsburgh.

The aim of the project is to guide technological choices starting from the design stages in research and development laboratories. Thanks to advanced techniques in computational chemistry and artificial intelligence, SUSTECH researchers will be able to retrospectively analyse and also predict the risk, toxicity, bioaccumulation, environmental and health impact profiles of new molecules and materials before they become consumer products.

SUSTECH combines these analyses with the study of patents, tracing the history of inventions and their creators to understand why certain technologies that are harmful to health and the environment have been successful, while other, safer ones have not been developed. The result will be an open knowledge base and a set of predictive tools to guide public policies and industrial strategies towards a “toxic-free” future.

In concrete terms, the project will build a large global database, linking for the first time the information contained in patents with predictive models of environmental and health risks. On this basis, using computational chemistry methods based on quantum mechanics, simulations will be developed to calculate the degree of danger of each new molecule and to identify the economic, scientific and regulatory factors that favour or hinder the emergence of more sustainable technologies. All the results will be brought together in an open access digital platform, designed to provide concrete tools to those who design, evaluate or regulate innovation: scientists, institutions, businesses and public decision-makers.

The creators of SUSTECH are: Elisa Giuliani, professor of Management and Vice-Rector for Sustainability at the University of Pisa, one of Europe's leading experts in the economics of innovation and sustainability; Arianna Martinelli, economist at the Sant'Anna School of Advanced Studies, specialising in patent analysis and the study of innovation trajectories and technological change processes; Stefan Wagner, professor at the University of Vienna and a leading figure in studies on patent economics and the direction of technical progress in the chemical-pharmaceutical sector; Olexandr Isayev, professor of Chemistry at Carnegie Mellon University, an expert in computational chemistry and few-shot models for predicting the toxicity and other socio-environmental impacts of molecules.

SUSTECH consolidates a line of research started over five years ago, combining computational chemistry and analysis of innovative trajectories through patents. This collaboration strengthens the link between the University of Pisa and the Scuola Superiore Sant'Anna, bringing together complementary skills developed over time by Elisa Giuliani, Arianna Martinelli, Gianluca Biggi and other researchers from the two universities.

ERC Synergy Grants are a type of funding from the European Research Council (ERC) intended for research groups of excellence, composed of two to four Principal Investigators, who present highly innovative and ambitious scientific projects. Their aim is to promote frontier knowledge through close and synergistic collaboration between scientists with complementary skills, capable of tackling complex challenges that none of them could solve individually.