Departing from the notion of reaction templates, the retrosynthesis problem can be treated as fully data-driven. This concept was first explored by Liu et al. . In their work, retrosynthesis was performed via a sequence-to-sequence translation problem, inspired by natural language processing (NLP). The molecules were featurised using SMILES as shown in Figure . SMILES is a 1-dimensional representation of a molecule consisting of individual symbols known as tokens, representing atoms and bonds. The authors utilised the tokenised product SMILES and a bi-directional short-long term memory (LSTM) to generate the reactants in an autoregressive fashion. Mathematically, the model selects the token sequence x with the largest joint probability as follows: