We trained the single-step retrosynthesis prediction models based on the seq2seq Transformer architecture using the OpenNMT-py package. All hyperparameters used here are provided in the SI §S1 and are based on the work of Pesciullesi et al. We trained the baseline model on only the General dataset. As fine-tuning and multi-task learning have been previously used for reaction prediction, we adopted the parameters previously reported for these models. For the multi-task model, we used a dataset weight ratio of 9 (General ):1 (Ring). For the fine-tuned model, the number of fine-tuning steps was set to 6,000. For mixed fine-tuning, a 1:1 dataset weight ratio and 6,000 fine-tuning steps were chosen after a benchmark (SI §S7). Ensemble decoding was performed with in-built OpenNMT-py functionality using the fine-tuned model and the baseline model.