To mitigate these limitations, data-driven approaches can be used to leverage statistical models and link molecular descriptors with relevant data. These descriptors, derived from molecular modeling and structural properties, are paired with solubility data collected from experiments. As a result, the target values inherently covers all realistic effects, enabling the application of various data-driven models such as machine learning (ML) methods to efficiently predict solubility .