Which is the effect of different types of molecular fingerprints for exploring the chemical space of natural products? Have a look here: https://doi.org/10.26434/chemrxiv-2023-0m355
We have published a comprehensive review on classification-based chemoemtric approaches to predict taste of molecules, have a look!
Rojas, C., Ballabio, D., Consonni, V., Suárez-Estrella, D., Todeschini, R. (2023) Classification-based machine learning approaches to predict the taste of molecules: a review. Food Research International, 171, 113036 [link]
Here our latest poublication, which was the first product of the PhD project of Emmanuel Cruz in collaboration with the Laboratory for Provenance Studies (University of Milano-Bicocca), the Department of Analytical Chemistry (University of the Basque Country) and IKERBASQUE (Basque Society for the Promotion of Science):
Cruz Muñoz, E., Gosetti, F., Ballabio, D., Andò, S., Gómez-Laserna, O., Amigo, J.M., Garzanti, E. (2023) Characterization of pyrite weathering products by Raman hyperspectral imaging and chemometrics techniques. Microchemical Journal, 190, 108655 [link]
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