In the preliminary stage the hyperparameters (see Supplementary Materials, Table ) and architectures (see Supplementary Materials, Figure ) of the models were optimized using the METLIN dataset, which was split differently from the main training-prediction-filtering stage. The unique molecules in the METLIN dataset were divided using a 5-fold cross-validation pattern (with the same fixed seed for all models and iterations), where four parts were used for training and one part for prediction. This selection process was repeated five times to generate a fully predicted copy of the METLIN SMRT database for each model.