Molecule-Edit Templates for Efficient and Accurate Retrosynthesis Prediction

Retrosynthesis involves the strategic breakdown of complex molecules into simpler precursors, paving the way for the synthesis of novel molecules. Recently, there has been a development of AI-based methods for retrosynthesis, which allow learning reaction rules from the data of historically performed reactions. A central component of such systems is a model for single-step retrosynthesis that predicts what reactions could lead to a considered target molecule. Two dominant methodologies are used for single-step retrosynthesis. Template-based methods use a set of translation rules that represent the possible chemical transformations. Although these methods are characterized by speed and interpretability, they may require an extensive set of templates to cover a large space of chemical reactions, which limits their generalization capacity. Conversely, template-free approaches can produce arbitrary reactions without such constraints but are often computationally demanding, largely due to their dependency on autoregressive decoding [1, 2, 3, 4].