The total number of compounds in the dataset includes 1039 organic compounds and 26 inorganic compounds. The compounds were analyzed based on the most common functional groups they contain. The quantitative distribution of these functional groups in the full dataset is presented in Figure . The histogram shows that structures with aromatic, carbonyl, halogen, and hydroxyl groups are the most common, while those containing phosphate, sulfo-, or nitro-groups are significantly less common. The chemical space of the full and drug-like subsets was analyzed using t-distributed stochastic neighbor embedding (t-SNE) (Figure ). The first two projections generated by t-SNE captured most of the variation present in the dataset, which was originally represented in a high-dimensional space derived from the Extended Connectivity Fingerprint (ECFP) with a circular neighborhood radius of 2, encoded in a 2048-bit vector using RDKit. The resulting scatter plot reveals that the compounds in the drug-like subset are clustered into a more compact region, indicating a higher degree of molecular homogeneity within this subset. In contrast, the full set displays a broader distribution, reflecting a greater diversity of chemical structures.