Similar to other fields, the foundation for successful machine learning applications in synthetic organic chemistry is the availability of extensive experimental data . Recently, Strieth-Kalthoff et al. demonstrated the benefit that emerges from the usage of real experimental data for machine learning-based chemical yield predictions while the prediction of reaction outcomes and yields remains a challenge in general . Nonetheless, there have been impressive developments using attention-based deep learning methods to explore the chemical reaction space . Schwaller et al. trained a transformer to predict chemical reaction outcomes with state-of-the-art results . The resulting model which is referred to as molecular transformer was then used in combination with hypergraph exploration to automatically plan retrosynthesis routes . Since then, the molecular transformer has been extended to predict the products of enzymatic reactions . Based on the aforementioned retrosynthesis planning system, Probst et al. have published a biocatalysed synthesis planning system .