In today's world of scientific infrastructure, there is a lot of talk about the compliance of data with FAIR data principles. We decided to test the QsarDB data archives for compliance with FAIR data principles and we are pleased with the FAIRness of 75% for data and models in QsarDB.
The last quarter of 2020 brought a new community from Slovenia. Laboratory for Cheminformatics at National Institute of Chemistry (NIC). NIC has a long history for providing in silico tools for mechanistic and empirical modelling applicable for drug design, assessment of toxicity, and materials optimization.
Each model in QsarDB can be used for prediction if the user calculates or determines experimentally the descriptors in the model and enters them in the “QDB Predictor” application [1] or via the REST service [2]. For some models, the prediction service also works directly from the structure. In this case, the QsarDB repository calculates the required molecular descriptors on the fly. Currently, QsarDB supports this functionality for a limited number of models, and they use open source software (Chemistry Development Kit, PaDEL-Descriptor and XlogP3) to calculate molecular descriptors.
In 2020, the QsarDB team celebrated Europe Day (May 9) in a modest but professional way. Namely, on that day, a new community was opened for the European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), which is a part of the European Commission's Joint Research Center (JRC) in Ispra, Italy.
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.