To develop and apply a ML model to complex molecule synthesis, we devised the following workflow (Figure ): (1) a library of literature examples was collected and annotated with chemical descriptors from simple and readily conducted DFT calculations ( ); (2) different machine learning model architectures were trained and evaluated for predictive performance; (3) human-generated retrosynthetic disconnections were evaluated using the trained ML model; (4) for the selected disconnection, substituents and functional groups were virtually screened with the model.