Recent years have witnessed an explosion in research applying neural network models to cheminformatics tasks. Sequence-to-sequence models, such as the Transformer and models based on the Recurrent Neural Network (RNN) architecture , are well suited to tasks such as direct reaction prediction, retrosynthesis prediction and molecular optimisation. Applying molecules encoded using Simplified Molecular Line Entry System (SMILES) to the Transformer model has produced state-of-the-art results on benchmark datasets for these tasks . Transformers have also been successfully applied to discriminative tasks such as biological activity prediction (virtual screening) and molecular property prediction (QSAR modelling) . Training Transformer models on SMILES strings, however, can be computationally expensive; a recently proposed model for direct synthesis prediction requires two days of training . Additionally, separate models must be built, trained and tuned for each task, increasing the amount of effort required by research teams.