Machine learning (ML) techniques are becoming routine tools in various research areas including the management of chemical experiments. A number of studies are based on the ML applications for predicting various physicochemical properties of chemical compounds, including LogP, 1-3 solubility, boiling point, spectroscopic and spectrometric data. The ongoing development of ML approaches and the accumulation of data have resulted in the creation of highly accurate models that are integrated into decision-making processes.