TOX-AI : Digitalising toxicological databases using Artificial Intelligence and in silico tools for food safety

The food that we eat contain an increasing diversity of chemicals. Some are added deliberately to enhance productivity or properties of food products, others are contaminants. The aim of this PhD research project is to develop a modelling platform to assess the safety of chemicals present in food. This platform will be developed based on mathematical modelling and explores the use of Artificial Intelligence, harnessing data advances from state-of-the-art databases, such as the OpenFoodTox, first to screen and prioritise substances of higher concern, and then to evaluate in a more detailed way those of higher concern.
The PhD project will have full access to a series of models available in VEGA, to predict (1) genotoxicity; (2) carcinogenicity (3) developmental toxicity, (4) reproductive toxicity; (5) endocrine disruption; (6) No Observed Effect Levels (NOAEL): (7) Threshold of Toxicological Concern (TTC); and (8) oral adsorption. The uncertainty of the predictions will be used to rank substance with higher concern, due to higher reliability in the prediction.
This PhD project will be carried out in close collaboration with researchers at the Food Standards Agency (FSA), the European Food Standards Authority (EFSA), and the Mario Negri Institute, Milano, Italy. Thus, the PhD student will be well supported by supervisory team with cross-disciplinary expertise and be exposed to an inclusive yet collaborative learning environment.