New master thesis in chemometrics!

We welcome Leonardo Fedrigotti, Martina Barbagallo and Davide crucitti, who will start their master thesis on chemometrics related topics. In particular, Martina will develop and validate proper chemometrics strategies based on advanced neural networks and deep-learning models to predict the molecular structure of some substances starting from their LC-MS/MS spectra, which were previously organized in a database. Davide will work in the field of QSAR, developing a model to classify substrates, inhibitors and non-active compounds of P-Glycoprotein using artificial neural networks. His project will be carried out at the Slovenian National Institute of Chemistry. Leonardo will develop a model which is able to couple GC-MS data and features of encephalographic responses (EEG) measured with portable low-cost devices to assess odor-stimulated emotions and to ensure scientific measurements of such signals. Coupling and comparison of analytical sources will be carried out through chemometrics methods. This project will be carried out at P&G (Bruxelles).

Premio Squinzi

Congratulations to Pietro Bertani for the “Premio Giorgio Squinzi” for his master thesis “A multivariate approach at the thermodynamic properties of polyamino polycarboxylic complexes with paramagnetic and other endogenous metal ions” (which was developed in collabroation with Bracco) and congratulations also to his supervisors Roberto Todeschini and Alessandro Maiocchi.

NURA: a curated dataset of nuclear receptor modulators

NURA (curated NUclear Receptor Activity dataset) is a curated dataset of nuclear receptor modulators. It contains bioactivity annotations for 15,206 molecules and 11 selected Nuclear Receptors (NRs) obtained by integrating and curating data from toxicological and pharmacological databases. The data can be dowloaded at Zenodo:; details on this dataset can be found here: Valsecchi, C., Grisoni, F., Motta, S., Bonati, L., Ballabio, D. (2020) NURA: a curated dataset of nuclear receptor modulators, Toxicology and Applied Pharmacology, 407, 115244 [link]

Geographical identification of Chianti red wines

This scientific publication was the outcome of a long collaboration with Ruffino, check it out: Bronzi, B., Brilli, C., Beone, G.M., Fontanella, M.C., Ballabio, D., Todeschini, R., Consonni, V., Grisoni, F., Parri, F., Buscema, M. (2020), Geographical identification of Chianti red wine based on ICP-MS element composition, Food Chemistry, 315, 126248 [link]