Organic reactions are usually assigned to classes containing reactions with
similar reagents and mechanisms. Reaction classes facilitate the communication
of complex concepts and efficient navigation through chemical reaction space.
However, the classification process is a tedious task. It requires the
identification of the corresponding reaction class template via annotation of
the number of molecules in the reactions, the reaction center, and the
distinction between reactants and reagents. This work shows that
transformer-based models can infer reaction classes from non-annotated, simple
text-based representations of chemical reactions. Our best model reaches a
classification accuracy of 98.2%. We also show that the learned representations
can be used as reaction fingerprints that capture fine-grained differences
between reaction classes better than traditional reaction fingerprints. The
insights into chemical reaction space enabled by our learned fingerprints are
illustrated by an interactive reaction atlas providing visual clustering and
similarity searching.

通过使用基于变换器的模型，可以从不带注释的简单文本化学反应表示中推断出反应类别，并可用作反应指纹，其分类准确率达到了 98.2%，而且较之传统反应指纹更好地捕捉反应类别之间的差异。