By applying link prediction in the CRKG we can infer new links between non connected reactants. The natural next step is to predict the resulting de novo products. For this purpose, a molecular transformer model is used. This sequence-to-sequence method has been developed in the field of natural language processing . To use a transformer for this task, the reactions need to be encoded using a string format such as SMILES . In the present case, the input sequence represents the reactant pair, and the output sequence corresponds to the product. To this end, Chemformer , has been used and fine-tuned on the reaction SMILES from the CRKG. Chemformer is a Molecular Transformer model based which uses an encoder-decoder architecture and has been developed and applied for varied chemistry tasks, including discriminative tasks such as molecular property prediction and translation tasks such as retroand forward synthesis. The pre-trained models were made available with the publication and can be used for fine-tuning on other datasets. The training and performance of Chemformer on the tabular version of CRKG is presented in Section 3.2.