Antonella Di Pizio

Prof. Dr. Antonella Di Pizio (born 1984) is an Associate Professor for Chemoinformatics and Protein Modelling at the Department of Molecular Life Sciences within the TUM School of Life Sciences at the Technical University of Munich. Since 2018, she has led the Molecular Modeling group at the Leibniz Institute of Food Systems Biology at TUM in Freising, Germany. Her academic journey includes a PhD in Pharmaceutical Sciences from the University of Chieti, Italy (2012), followed by research at Philipps University in Marburg, Germany, and a postdoc position at the Hebrew University of Jerusalem, Israel.

Prof. Di Pizio's research focuses on computational approaches to understanding chemosensory G protein-coupled receptors (GPCRs), particularly taste and smell receptors. Her work combines molecular modeling, chemoinformatics, and structural bioinformatics to investigate the molecular basis of ligand recognition and activation mechanisms. Her group develops predictive models for screening and designing bioactive compounds relevant to food reformulation and therapeutic applications.

Analysis of Prof. Di Pizio's publication record reveals a strong focus on bitter taste receptors, particularly TAS2R family members, and odorant receptors. Her research spans computational modeling of receptor-ligand interactions, development of predictive algorithms for taste compound identification, and investigation of the structural basis of chemosensory perception. Recent work demonstrates increasing integration of machine learning approaches with traditional molecular modeling techniques.

Scientific Recognition:

• Leibniz Best Minds Programme for Women Professors (2022)
• Platinum Manfred Rothe Excellence Award in Flavor Research (2019)
• Bernardo Nobile doctorate award VIII Edition (2013)
• Keystone Symposia Future of Science Fund Fellowship (2017)

Prof. Di Pizio serves on the editorial board of Frontiers in Molecular Biosciences and is a Working Group Leader and Management Committee member of the ERNEST Cost Action CA18133. She teaches courses including 'Modeling and simulations of Biological Macromolecules' and 'Drug and Protein Design' at TUM. Her Molecular Modeling group collaborates extensively with other research groups at the Leibniz-LSB@TUM on interdisciplinary projects focused on food systems biology.

The Molecular Modeling group, established relatively recently at the institute, investigates food-relevant molecules and their interactions using computational tools including molecular docking, molecular dynamics simulations, pharmacophore modeling, QSAR, machine learning, and virtual screening. Their work aims to develop next-generation methodologies for food design that address current challenges in the food system.