Interpreting the developed models is a crucial step in gaining insights into the obtained results and understanding the underlying relationships within the data. The contribution of individual features to model predictions was analyzed using the SHapley Additive ex-Planations (SHAP) method. 52 SHAP provides a comprehensive framework for interpreting machine learning model outputs, leveraging principles derived from cooperative game theory to fairly attribute the contribution of each feature to the final prediction. By applying SHAP values, it is possible to quantify the impact of each molecular descriptor on solubility predictions, thereby enhancing the interpretability and transparency of the developed models.