Comparison to existing literature in the domain showed that the model trained solely on the USPTO dataset was competitive with that reported in the literature (Figure ), and was able to find a route to the target compound in 4.26 seconds. This was also observed for models trained on the subset of the AZ ELN, Pistachio and Reaxys datasets. We found that the model was able to suggest an alternative route in addition to that reported, involving a ring formation (Figure ). Furthermore, we show that the model can predict routes to the top 125 pharmaceutical products, where the performance is dependent on the stock set of compounds. Examples of which have been given in the supplementary information. The route predicted using the model trained on the USPTO dataset to Amenamevir is compared to the literature route. Both routes vary in the order of the steps they take, with the predicted route preferring a standard amide coupling over the amide Schotten-Baumann. The model trained on the USPTO dataset, finds an alternative route to that in the previous study, and finds synthetic routes to the target compound in 4.26 seconds. The model can prioritize and apply ring formations as demonstrated in Step 4. b) Comparison of the route found by the model trained on the USPTO dataset with the literature route for Amenamevir. The model can suggest a route comparable to the literature, differing in the sequence of steps and using similar reactions to those in the literature. The predicted route is found in 3.26 seconds.