To learn about chemical properties that are challenging for log P predictions, we analyzed the prediction errors of the molecules (Figure ). We chose to use MAE for this analysis because it is less affected by outliers compared to RMSE and is therefore more appropriate for following global trends. Although methods varied in performance, as indicated by large and overlapping confidence intervals, the MAE calculated for each molecule as an average across all methods indicates that some of the molecules were better predicted than others (Figure ). For reference, compound classes and structures of the molecules are available in Figure . Molecules such as SM26, SM27, and SM28 were well predicted on average. Molecules such as SM42, SM43, and SM36