In recent years, the interest in predictive (machine learning) models for chemical reactivity has soared. For tasks for which hundreds of thousands to millions of data points are available, e.g., forward and retrosynthesis prediction, accurate and robust machine learning (ML) models have been designed. In the absence of data scarcity, neural network architectures that learn from simple structural descriptions of molecules can be adapted to these respective tasks with great success. For example, both graph neural networks (GNNs) operating on 2D molecular graphs and Transformer models operating on Simplified Molecular Input Line ‡ Additional footnotes to the title and authors can be included e.g. 'Present address:' or 'These authors contributed equally to this work' as above using the symbols: ‡, §, and ¶. Please place the appropriate symbol next to the author's name and include a \footnotetext entry in the correct place in the list.