We also investigate the model performance across different chemical classes, and illustrate the results in Figure . To analyze the impact of chemical classes on model performance, we categorize molecules based on their chemical structures using SMARTS patterns and substructure matching. It is important to note that the overall number of molecules per class is very low (sometimes as few as one), indicating that additional data and further evaluations will be needed to confirm these findings. In Figure , the boxes represent the interquartile range with lines indicating the median values and the whiskers extend to 1.5 times the interquartile range. The EXT-Zamora dataset features a diverse set of chemical classes, including 11 phenols, 5 ketones, 3 quinoline, 3 ethers, 3 alcohols, 2 benzoic acids, 2 alkyl halides, and one each of aminophenol, aniline, benzene derivative, and cycloalkane (5 molecules classified as other). The EXT-SAMPL9 dataset, in contrast, is less diverse compared to EXT-Zamora dataset. It is a smaller dataset comprising a limited range of chemical classes, containing 4 pyridine derivatives, 2 benzene derivatives, 2 anilines, and one each of phenol, ureide, ketone, aminophenol, and sulfonamide (3 molecules classified as other). An overview of the chemical class distributions in the LF-QC and HF-Exp datasets can be found in the supporting information.