The first wave of Deep Learning methods for molecule generation ) used the SMILES ) representation of compounds in combination with recurrent neural networks (RNNs) , which are well suited to process sequences. More recent versions of these models have moved towards more robust line notations of chemical structure, such as DeepSmiles (O'Boyle and Dalke 2018) or SELFIES . Another range of models directly generate molecular graphs, using graph neural networks . Apart from the utilized representation, models differ in the training procedure and model architecture. For a more detailed overview on current approaches, see .