The generated results for the different methods were tested for statistical significance as follows. The R 2 and RMSE values generated for each cross-validation split were analysed with post-hoc Conover-Friedman test, and to account for the multiple comparisons, the two stage Benjamini-Yekutieli padjustment procedure was used. Statistically significant differences between the best and worst performing methods were generally observed for the random data splits (Figures ). To evaluate the importance of the type of the molecular representation used, the best models for each representation were compared, selected separately for each data source and training size (Tables ). The performance of the best models built using the random split for different representations was comparable, with no noteworthy statistically significant differences (Figures and).