Furthermore, the seeds for the train-prediction splits were set randomly for each of the three runs. The dataset plays the key role in the training process, therefore it is crucial for our approach to be reproducible with different train-prediction splits. The average number of potentially erroneous retention times was 1534 ± 14, with the intersection of three sets of 1299 entries or 85%. This set of 1299 SMILES for potentially erroneous entries can be found in Supplementary Materials (separate text file) and on GitHub . This result underlines the stability of our approach with changing train-prediction splits. The variance between the sets can be attributed to some of the retention times being predicted inaccurately, as will be described below. Data from these three runs were used further to calculate statistics and generate plots.