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).
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.
Todeschini, R., Consonni, V., Ballabio, D., Grisoni, F. (2020) Chemometrics for QSAR Modeling. In Comprehensive Chemometrics (Second Edition), S. Brown, R. Tauler, B. Walczak (Eds.), Elsevier [link]
Here a new chapter just published in the Encyclopedia of Analytical Chemistry (Wiley) on distance and simialrity measures for chemical data: Todeschini, R., Ballabio, D. and Consonni, V. (2020). Distances and Similarity Measures in Chemometrics and Chemoinformatics. In Encyclopedia of Analytical Chemistry, R.A. Meyers (Ed.) [link]
New Matlab toolbox available for the calculation of high level data fusion (consensus) approaches (majority voting and Bayes consensus with discrete probability distributions): https://michem.unimib.it/download/data/bayes-and-majority-voting-consensus-for-matlab/
Our latest publication on data fusion, consensus and QSAR is available on line: Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D. (2020), Consensus versus individual QSARs in classification: comparison on a large-scale case study, Journal of chemical information and modeling, 60, 1215-1223 [link]
This publication was the outcome of a great collaborative project for the identification of Androgen Receptor Activity through machine learning and consensus analysis, check it out: Mansouri, K. et al. (2020) CoMPARA : Collaborative Modeling Project for Androgen Receptor Activity. Environmental Health Perspectives, 128, 027002 [link]
Available PhD position for Molecular Modeling and Virtual Screening for Rational Design of Tubulin-Protein Interaction Modulators at TubInTrain (European Joint Doctorate on chemistry and biology). For further info: https://www.tubintrain.eu/phd-project-esr2/
The valedictory lecture of Prof. Todeschini will take place on monday 16th of december 2019, 10:30, room U3-01 (building U3 – Università degli Studi di Milano – Bicocca): “Una vita per la ricerca, la ricerca di una vita” [link]
In this new release (5.4), class modelling methods (SIMCA, Potential Functions – Kernel Density Estimators, UNEQ) can now be calculated on a specific target class. The toolbox can be downloaded here.