Bidirectional Graphormer for Reactivity Understanding: neural network trained to reaction atom-to-atom mapping task

This work introduces GraphormerMapper – a new algorithm for reactions atom-to-atom mapping (AAM) based on a distance-aware BERT neural network. In benchmarking studies with IBM RxnMapper, the best AAM algorithm according to our previous study, we demonstrate that our AAM algorithm is superior on our “Golden” benchmarking dataset. The mapper is implemented in Chython [https://github.com/chython/chython] and Chytorch [https://github.com/chython/chytorch, https://github.com/chython/chytorch-rxnmap] Python packages which are freely available for out-the-box use. Chython is a cheminformatics library with a simple interface for processing reaction and molecular data. The key features of Chython are: chemical functional groups standardization, checking atom valence errors, substructure search, and advanced reaction manipulation, for example, generating products from reactants and reaction atom-to-atom mapping. Chytorch provides a PyTorch-like interface for graph-based neural networks developed specifically for chemical tasks.