In the case of aqueous solubility, the use of molecular descriptors such as diverse molecular descriptors characterizing the chemical structure or its properties has been shown to give the most accurate prediction so far. Sorkun et al. achieved notable predictive accuracy by using a dataset with 4399 unique data points, a feature set with 123 descriptors, and a consensus model with Random Forest, XGBoost, and an artificial neural network. Some major challenges consist of the existence of measurement noise and data quality issues, the diversity and scale of the molecular space, and the broad range of solubility values.