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]
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]
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]
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]
New publication: Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making, have a look here!
Have a look to our latest publication: Grisoni, F., Consonni, V., Ballabio, D. (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project. Journal of chemical information and modeling, 59, 1839-1848 [link]
Data related to these models are available for download.
New chapter: Recent advances in High-Level Fusion Methods to classify multiple analytical chemical data (published in in Data Fusion Methodology and Applications, Elsevier).
Have a look!!!
Glad our publication is now availale: On the misleading use of Q2F3 for QSAR model comparison, Molecular Informatics (2019), 38, 1800029. Have a look!