We selected a subset of reactions from the USPTO dataset, excluding reactions used in the reference routes, to train one-step retrosynthesis models similar to the model trained by Thakkar et al. . We trained one model that was used in the set-n1 experiments and one that was used in the set-n2 experiments, although we could have trained one model on the intersection of the two datasets with some reduction in the number of reactions. The dataset consists of 878,079 and 871,001 reactions for the set-n1 and set-n5 routes respectively, distributed over 46,092 unique reaction templates. The extracted templates were one-hot encoded as output to a neural network. The input was the extended connectivity fingerprints of the product molecules calculated by RDKit [37] using a radius of 2 and a length of 2048. The neural network was a simple feedforward network: 512 nodes with an ELU activation function, and L2 normalization, followed by a dropout later with a dropout rate of 0.4 and finally an output layer with a softmax activation function. The model was trained for 100 epochs and a batch size of 256. The Adam optimizer was used with a categorical cross-entropy loss. The learning rate was initially set to 0.001 and was halved upon a plateau of the validation loss after 5 epochs. The dataset was split by 90%/5%/5% into training, validation, and test set, respectively.