Recurrent neural networks RNNs are used to create molecules without the use of input fragments. These molecules are generated in the form of tokenized SMILES sequences. The RNNs are built from long short-term memory (LSTM) units or gated recurrent units (GRUs). The RNN model consists of the following layers: an embedding layer, three recurrent layers, a linear layer and a softmax activation layer. These building blocks are trained to predict the most likely next output token. Compared to the transformer models this generator does not require inputs, is quick to train and still has a relatively low error rate.