Then, a timesplit of a filtered set of molecular pairs into training, validation and testing subsets was performed. The timestamp for each molecule was defined as the year of the first publication that mentioned this molecule (as reported in the ChEMBL). training set was formed by pairs in which both molecules had timestamps before a train year threshold. A test set was defined as at least one molecule in each pair was experimentally published after a test year threshold. A validation set was comprised of all remaining pairs. We used the train and test cutoff years of 2013 and 2015, respectively, which ensures an approximate 2:1:1 ratio of the sizes of the training, validation and test subsets, respectively. In each subset, SMILES representations of molecules were converted to SELFIES. Model -We adapted the transformer model from OpenNMT, an open-source neural machine translation project using PyTorch, with its default settings (unless explicitly stated) for our generative chemistry project. We chose this software because it is actively maintained and has a high number of users in various fields of application. The batch size of 128 was used (decreasing the batch size to 16 did not improve the performance of trained models but slowed down training; data not shown). All our programs for preprocessing data, training a model, using a model to make predictions, and postprocessing results were written in Python and ran with SLURM scripts in a High-Performance Computing cluster at Pfizer. Models were trained on one GPU of a 4-GPU (NVidia V100) node.