Transformers are another type of NN architecture that transform an input sequence of data to an output sequence. A self-attention mechanism is used to handle long-range dependencies in sequential data, enabling effective modelling of the complex relationships present. Transformers have been used for a wide variety of different tasks, including natural language processing, 62 image recognition and speech recognition, and in chemistry for synthesis and retrosynthesis, 65 molecular property prediction and reaction prediction . For more information on transformers, please refer to Ref. .