RESUMEN
In this study, we utilized 2H SNIF NMR and chemometric techniques to differentiate the botanical origin of raw materials used in vodka production in Poland, specifically focusing on plants with C3 metabolism such as grain, potato, and sugar beet. Additionally, for the first time, mixtures of alcohols from different C3 plants were analysed to detect adulteration. Our goal was to determine if it is possible to detect the addition of a different raw material in vodka made from a mixture of alcohols derived from C3 plants. Significant isotopic differences were confirmed using analysis of variance and Tukey's tests. Linear relationships in grain-potato, grain-sugar beet, and beet-potato mixtures enabled composition determination. The detectability threshold for adulterants ranged from 10 % to 50 %, depending on the type of raw material. Our findings suggest that 2H SNIF-NMR is an effective tool for authenticating vodka and detecting adulteration in products marketed as "Polish vodka."
RESUMEN
The current pandemic outbreak clearly indicated the urgent need for tools allowing fast predictions of bioactivity of a large number of compounds, either available or at least synthesizable. In the computational chemistry toolbox, several such tools are available, with the main ones being docking and structure-activity relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein of SARS-CoV-2 virus with ACE2 human receptor interface. Based on over 1800 structures in the training set we have predicted binding properties of the complete set of nearly 600000 structures from the same class using the Machine Learning Random Forest Regressor approach.