April is the right time to review QsarDB citations from 2022 because almost all scientific literature databases (Web of Science, Scopus, ScienceDirect, Google Scholar, etc.) have more or less indexed the scientific articles published in the past year.
Many thanks to everyone who has considered QsarDB.org important and necessary in their work and research!
It is extremely important for the repository that the data stored in it, and in our case also the models, are used and create new knowledge. Thus, in 2022, models from QsarDB have been used five times, either for prediction, data analysis or to create new QSAR-s. The melting point model has been used two times (1,2) to predict meting point of drugs substances. This melting point prediction model was derived by Jean-Claude Bradley and Andrew Lang of Oral Roberts University guided by the Open Notebook Science paradigm and has been available for prediction since 14 June 2012. In another example, the categories of OECD QSAR principles have been analyzed for 18 models with respect to the size of the data series and the type of model. In the last two cases, data from the repository have been used to derive a new model for the bio-concentration factor and the intestinal permeability of compounds of pharmaceutical interest. QsarDB was also cited fifteen times in 2022 in various reviews and regular research articles. One can get a full overview from the list of citations
However, we would like to draw your attention to the correct use of data citations for the models in the repository. Examples are provided in the guide based on the recommendations of publishers. The correct data citation is also displayed on each archive page, and it’s easy to copy it into the manuscript.
The 2022 review has been compiled based on the best available information, if something has been overlooked, please write qsardb@chem.ut.ee.