Our recent paper RXNMapper-v2: updated validation and evaluation on expanded chemical space in Digital Discovery!

Atom-to-atom mapping (AAM) assigns reactant atoms to product atoms and is fundamental for reaction classification, template extraction, and synthesis planning. RXNMapper derives AAM from the attention weights of a transformer model trained by masked language modeling on unmapped reaction SMILES. However, its original model selection relied on USPTO-50K, a validation set later shown to contain erroneous mappings. Here we revisit RXNMapper using a curated validation set of 4′533 reactions from the dataset Pistachio. We retrained ALBERT-based RXNMapper models on USPTO-full and Pistachio and selected the best head, layer, and checkpoint on this new validation set. To improve the comparison of AAM algorithms, we developed an evaluation framework that removes ambiguous reference reactions and compares predictions based on reaction graph isomorphism rather than direct string matching, avoiding false negatives from inconsistent formats and symmetry-equivalent mappings. Across the golden dataset, Pistachio, and an internal AstraZeneca medicinal chemistry benchmark, the updated models outperform the original RXNMapper and LocalMapper, while rearrangements remain persistent challenges.

Author(s) Yves Grandjean, Sacha Raffaud, Annie M. Westerlund, Philippe Schwaller, Samuel Genheden and Jean-Louis Reymond