New approach methodologies for risk assessment using deep learning.

The advancement of technologies and the development of more efficient artificial intelligence (AI) enable the processing of large amounts of data in a very short time. Concurrently, the increase in information within biological databases, such as 3D molecular structures or networks of functional macromolecule associations, will facilitate the creation of new methods for risk assessment that can serve as alternatives to animal testing. Specifically, the predictive capabilities of AI as new approach methodologies (NAMs) are poised to revolutionise risk assessment approaches. Our previous studies on molecular docking predictions, using the software Autodock Vina, indicated high-affinity binding of certain toxic chemicals to the 3D structures of human proteins associated with nervous and reproductive functions. Similar approaches revealed potential sublethal interactions of neonicotinoids with proteins linked to the bees' immune system. Building on these findings, we plan to develop an AI-based decision tool that exploits the data available on the toxicity of the most know chemical, such as LD50, and the data obtainable by their interaction with the human proteins to support risk assessment studies for multiple stressors still not characterised. Our focus will be on utilising these new bioinformatics methodologies to develop specific experimental designs that allow for confident and predictable study of the toxic and sublethal effects of pesticides on humans. We will also validate the developed NAMs by integrating existing in vivo information from scientific literature and technical reports. These approaches will significantly impact toxicity studies, guiding researchers' experiments and greatly reducing the need for animal testing.