For the METLIN SMRT dataset, the mean error varied from 50 s for the XGBoost model to 32 s -for GATv2Conv+DL. The latter value constitutes 2.7% of the whole scale of retained compounds (1170 s, or from 300 to 1470 s). Domingo-Almenara and co-authors found that the mean RT variability of the dataset was 36 s (for the entire dataset, not retained molecules only), and the peak full width at half maximum varied between 20 and 30 s. Thus, the current prediction models achieved errors that was very similar to the method errors. The 10-fold cross validation for METLIN SMRT prediction highlighted the GATv2Conv+DL model as the best-performing one.