Figure (a) presents the total number of molecules in each fold, along with the corresponding counts of functional groups. Figure (b) highlights the mean ratio of the number of perfect matches to the total number of functional groups grouped by the number of functional groups present in the molecule. The perfect match ratio is greater than 0.5 for molecules with at least one functional group and less than 6 functional groups. A very low number of perfect matches are observed for molecules with less than 1 and more than 6 functional groups. This trend correlates to the data distribution as depicted in Figure . As the dataset contains a higher number of samples with 1 to 5 functional groups, the model is better at classifying samples with similar distribution. Notably, a high FPR is observed for molecules lacking functional groups, consistent with the data distribution depicted in Figure , where there are very few data points without functional groups. The FPR and FNR curves intersect at the same value for samples containing three functional groups, aligning with the highest sample count in this category as shown in Figure . Overall, the FNR remains below 0.2 while the FPR stays under 0. (4-Fluoroanisole) with a SHAP (SHapley Additive exPlanations) analysis. The SHAP analysis is a powerful tool in machine learning interpretability, providing insights into individual feature contributions to model predictions .