The three other approaches also show improvement over the baseline model with top-1 reactant-accuracies of around 36% and 62% valid ring-breaking top-1 predictions. While the observed improvement over baseline isn't as high as reported in previous studies (13.6% increase in accuracy for fine-tuned model here vs 27.0% for carbohydrate reactions and 28.6% for Heck reactions), there are two key aspects to note. Firstly, the mentioned studies used transfer learning for forward reaction prediction, not retrosynthesis, which is considered to be a much easier task, only having one "correct" answer. Moreover, heterocycle formations are a much larger and more diverse class of reactions than Heck reactions or even carbohydrate reactions, making it more difficult for the model to learn all the different reactivity. Interestingly, even though each of our approaches increases the proportion of top-1 valid ring-breaking predictions by at least 7% when compared to the baseline model, the same trend is not observed when considering just the top-1 round-trip accuracy of the predictions (SI §S9). For example, for the mixed fine-tuned model, the ring-breaking round-trip accuracy increases by over 10%, while the round-trip accuracy decreases by 1%. The same trend can be observed for all other approaches apart from the multi-task model, where the round-trip accurcay increases but not as much as the ring-breaking round-trip accuracy (SIS9). This indicates that the main improvement between the various models trained using transfer learning and the baseline model is in the type of disconnection suggested, i.e. ring-breaking versus more common reaction types, and not in turning chemically invalid disconnections into valid ones. It also suggests that while the molecules in the Ring test set were synthesized using ring formation reactions, there are other chemically viable disconnections available. Indeed, comparing the predictions of the baseline and mixed fine-tuned model revealed that the former often suggested more common reaction types, such as functional group interconversions (FGIs) or protection/deprotections, instead of the ground-truth heterocycle formation predicted by the mixed fine-tuned (Figure ). For instance, in example 4A. the mixed fine-tuned model correctly identifies a click reaction to generate the triazole from two fragments of similar complexity. In contrast, the baseline model only suggests a more trivial N-alkylation reaction. Similarly, for 4B. the mixed fine-tuned model suggests a condensation reaction to form the central benzimidazole ring, while the baseline model suggests a functional group interconversion, which would be more suitable earlier in the synthetic route. In 4C. and 4D. the baseline model predicts simple halogenation reactions rather than ring disconnections.