The topic of Big Data has strongly entered into predictive and computational toxicology, and is here in order to stay. However, not only the data is big, large are also computational models (the so-called QSAR models) derived from these data. Therefore, the management of computational models in the context of Big Data needs more attention and efforts, so that the models remain transparent and independently verifiable.
The last quarter of 2019 brought two new communities to the QsarDB, namely, Ineris (France) and Chemoinformatics Research Group from Liverpool John Moores University (England).
19th International Workshop on (Quantitative) Structure-Activity Relationships in Environmental and Health Sciences will be in Durham, NC, June 8-11, 2020.

Lisa Bang has developed a simple QsarDB archive parser for the Python programming language. This parser can be useful to someone interested in reproducing the QSAR provided in the QsarDB archive format.
This summer, QsarDB’s main website underwent a major upgrade. The visual side was redesigned to have a modern look, but it was only a small part of the effort. Most of the work went into improving the documentation and its results are available in the new section, GUIDELINES.