In the following, we describe the methodology and details of the employed machine learning models for retrosynthesis, forward prediction and regioselectivity prediction, which we train on the EnzymeMap database. To this end, we have selected tasks that depend on atom mapped reactions (as opposed to tasks and models taking only substrate information into account). For each task, an open-source model architecture representing the current state-of-the-art was chosen, namely 1) the single-step retrosynthesis model from Ref. 31 which underlies the popular opensource synthesis planning tool ASKCOS, 2) the transformerbased single-step forward and retrosynthesis model of the successful IBM RXN model, and 3) the message-passing neural network Chemprop which recently was shown to produce highquality reaction predictions.