There are two general ways to solubility modelling: bottom-up and top-down. Bottom-up approaches are primarily based on molecular simulations . Top-down approaches are based on thermodynamic models and quantitative structure property relationship (QSPR) methods . Nowadays, QSPR methods for solubility predictions heavily use machine learning as statistical backbone. Machine learning based methods demonstrate high-performance, but rely entirely on the quality and amount of training data . Providing high-quality data is of great importance for further development of top-down approaches.