The sequencebased transformer model is a decoder-only transformer that applies a multi-headed attention architecture and position-wise feed-forward layers followed by a linear layer and an activation function to predict the most likely output token. In contrast to the RNN, the transformer models allow for user-defined inputs in the form of fragments. Furthermore, contrary to the graph-based transformer, the sequence-based transformer allows for easy incorporation of stereochemistry.