Phd in PHARMACEUTICAL SCIENCES

Thesis title: DIGITAL PLATFORMS AND COMPUTATIONAL CHEMISTRY METHODS: LINKING AGRI-FOOD DATA TO DRUG DISCOVERY AND STRUCTURAL BIOLOGY

The growing digitalization of the agri-food sector and the development of computational technologies for drug discovery share a common goal: transforming large and heterogeneous datasets into useful knowledge for sustainability, health, and innovation. In this context, the Agritech project and the METRIQA platform represent a key step toward creating an integrated digital space for collecting, analyzing, and enhancing agri-food data. These data — related to crops, chemical composition of products, and agricultural practices — can also be reinterpreted from a biotechnological and pharmacological perspective, offering new opportunities to identify bioactive compounds of natural origin or derived from plant waste. By leveraging artificial intelligence, machine learning, and molecular modeling tools, this research proposes an integrated approach that connects information from the agri-food domain with computational studies aimed at discovering new molecular inhibitors. In particular, the study focused on the kinase NEK6, an emerging target in several solid tumors, and on the protein CD44, which plays a key role in adhesion and metastasis processes. Through virtual screening, molecular docking, molecular dynamics simulations, and hit optimization of two synthetic molecules, both natural and synthetic compounds were identified and refined, showing favorable pharmacokinetic properties and binding affinities. The doctoral thesis highlights how the convergence between agri-food data and computational chemistry can accelerate the discovery of new bioactive molecules and promote the sustainable use of natural resources, paving the way for a future where digital agriculture and pharmaceutical research work together to develop innovative solutions for health and the environment.