Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task

This
work introduces GraphormerMapper, a new algorithm
for reaction atom-to-atom mapping (AAM) based on a transformer neural
network adopted for the direct processing of molecular graphs as sets
of atoms and bonds, as opposed to SMILES/SELFIES sequence-based approaches,
in combination with the Bidirectional Encoder Representations from
Transformers (BERT) network. The graph transformer serves to extract
molecular features that are tied to atoms and bonds. The BERT network
is used for chemical transformation learning. In a benchmarking study
with IBM RxnMapper, which is the best AAM algorithm according to our
previous study, we demonstrate that our AAM algorithm is superior
to it on our “Golden” benchmarking data set.