Category Archives: Scientific publications

Membrane introduction mass spectrometry (MIMS) is a direct mass spectrometry technique used to monitor online chemical systems or quickly quantify trace levels of different groups of compounds in complex matrices without extensive sample preparation steps and chromatographic separation. Here a recent review:

V. Termopoli, M. Piergiovanni, D. Ballabio, V. Consonni, E. Cruz Muñoz, F. Gosetti (2023) Condensed phase membrane introduction mass spectrometry: a direct alternative to fully exploit the mass spectrometry potential in environmental sample analysis, Separations, 10, 139, https://doi.org/10.3390/separations10020139

We have published a new chapter, which is a tutorial for training multitask neural networks in the framework of QSAR modelling, have a look here: https://doi.org/10.1007/978-3-031-20730-3_8

Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D., Todeschini, R. (2023). Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial. In Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. (Hong, H., eds), Springer

We have contributed with a chapter about chemometric methods used in the framework of food authenticity (Authenticity and Chemometrics basics) in the book Chemometrics and Authenticity of Foods of Plant Origin, CRC Press, here the link

Out a new publication: “From the streets to the judicial evidence: determination of traditional illicit substances in drug seizures by a rapid and sensitive UHPLC-MS/MS-based platform”, have a look here.

We have developed a new analytical method for the identification of photodegradation products of Escitalopram, details are published here: Termopoli, V., Consonni, V., Ballabio, D., Todeschini, R., Orlandi, M., Gosetti, F. (2022) Identification of photodegradation products of Escitalopram in surface water by HPLC-MS/MS and preliminary characterization of their potential impact on the environment, Separations, 9, 289 [link]

Multi-task neural networks and molecular fingerprints to enhance compound identification from LC-MS/MS data, Molecules (2022), 27, 5827 [link]. Data to reproduce the results are available at our website: https://michem.unimib.it/download/data/lc-ms-ms-to-fingerprints-dataset/

Our paper dealing with the application of multitask neural networks to predict molecular activity on nuclear receptors is now published, have a look here: https://doi.org/10.1002/cem.3325

We compared different appraoches for optimisation of multitask neural network hyperparameters on QSAR data, results were recently published in the following manuscript: Valsecchi, C., Consonni, V., Todeschini, R., Orlandi, M., Gosetti, F., Ballabio, D. (2021) Parsimonious Optimization of Multitask Neural Network Hyperparameters, Molecules, 26, 7254 Have a look here!

The manuscript about our latest partecipation in the CATMoS collaborative modelling project to predict Acute Oral Toxicity is not out, have a look: CATMoS: Collaborative Acute Toxicity Modeling Suite, Environmental Health Perspectives, 129, 47013 [link]