After the data filtering process, the acquired dataset consists of 23K molecules, each one of them is associated with four MS/MS spectra or more precisely up to four MS/MS spectra given that there are cases with missing spectra. This dataset was partitioned into a training, a validation and a test set with the validation and test set having about 1K molecules each. For the test set specifically, we used fingerprint similarity, based on the Tanimoto coefficient , in order to ensure that no test molecule is either in the train or in the validation set. The validation set was used to select the model hyper-parameters and the test set was used to evaluate the performance of the model.