In terms of reactant-accuracy, both the mixed fine-tuned and the template-based model have similar top-1 reactant-accuracies (Figure ), with the template-based model's reactantaccuracy being slightly higher. However, the mixed fine-tuned model has significantly higher top-1 round-trip accuracy. These trends remain consistent across top-3 and top-5 predictions (SI §S9). Moreover, the round-trip accuracies for the template-based model decrease rapidly from top-1 to top-5, from 53.4% to 31.8%, while the mixed fine-tuned model maintains high round-trip accuracy from top-1 (74.6%) to top-5 (71.8%) (Table ). The mixed fine-tuned model also suggests a higher overall proportion of chemically valid ring-breaking disconnections (defined in Methods), with 59.9% for the mixed fine-tuned model compared to 30.8% for the template-based model in the first 5 predictions (SI §S9). Additionally, the mixed fine-tuned model maintains considerable accuracy on the General test set, while the template-based model achieves a low top-1 accuracy of 0.5%. Furthermore, we observe that the template-based model generates a larger proportion of non-admissible predictions of 'None', with 48.8% of top-5 predictions being inadmissible, Overall, our results demonstrate that the mixed fine-tuned model significantly outperforms the template-based model in round-trip accuracy, suggesting more diverse disconnections for both general and ring-breaking disconnections, making it the preferred choice for multi-step retrosynthesis as discussed in the following section. However, it is important to note that the forward reaction prediction model used for calculating round-trip accuracy is of the same architecture as the mixed fine-tuned model and is trained on the same reaction data (but with reversed labels). This could be biasing the metric towards the mixed fine-tuned model and mean that the difference in round-trip accuracy between the mixed fine-tuned model and the template-based model is not as significant as it seems. A more objective way of calculating metrics such as round-trip accuracy could be to use a different model to predict reaction viability instead of the forward reaction prediction model, however we were not able to train such a model for this work due to lack of negative reaction data.