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/
Category Archives: Scientific publications
Multitask neural networks to predict molecular activity on nuclear receptors
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
Parsimonious optimization of multitask neural networks
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!
New publication on Collaborative Acute Toxicity Modeling
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]
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: https://doi.org/10.5281/zenodo.3991561; 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]
New book chapter: Chemometrics for QSAR Modeling
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]
New chapter on distance and similarity measures
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]
Consensus to enanche QSAR modelling: a large-scale case study
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]
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity
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]
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]