Recurrent neural networks are deep neural networks composed of layers of either long short-term memory units or gated recurrent units, which store and transfer information from one state to the next. In de novo molecule generation, SMILES symbols are one-hot encoded into a binary vector which is used as input to the network. These networks are then trained to predict the conditional probability of a SMILES subsequent symbols given a sequence of previously seen SMILES symbols. This is achieved by training the network using maximum likelihood estimation (equivalent to minimizing the negative log likelihood), whereby the model must maximize the likelihood assigned to the correct symbol 𝐱 at time 𝐭 conditional upon all previously observed symbols. The resulting loss function L parameterized by the network parameters θ is shown in Equation . For further details we refer the reader to .