Notable methods are long short-term memory (LSTM) neural network, 30 transformer, bidirectional encoder representations from transformers (BERT), etc. We should note that NLP is particularly promising in bioinformatics due to the natural fit to convert enzymes into amino acid letters and reactions into machine readable string representations. For reaction prediction, Kreutter et al. developed an enzymatic transformer, to convert enzymatic reactions into reaction SMILES and enzymes into language tokens. The transformer predicts reaction products with remarkable accuracy. For biosynthetic planning, Probst et al. generalised Molecular Transformer, a deep learning reaction prediction transformer model, to predict biocatalytic reaction outcomes and build pathways. Ofer et al. discussed the possibility to use the protein sequence, the amino acid chain, as a language and reviewed a number of protein-related tasks solved by NLP methods. NLP certainly can provide insights to tackle bioinformatics problems.