Computer-aided synthesis design, automation, and analytics assisted by machine learning are promising resources in the researcher’s toolkit. Each component may alleviate the chemist from routine tasks, provide valuable insights from data, and enable more informed experimental design. Herein, we highlight selected works in the field and discuss the different approaches and the problems to which they may apply. We emphasize that there are currently few tools with a low barrier of entry for non-experts, which may limit widespread integration into the researcher’s workflow.

Chemical reactions can be classified into distinct categories that encapsulate concepts for how one molecule is transformed into another. One can encode these concepts in rules specifying the set of atoms and bonds that change during a transformation, which is commonly known as a reaction template. While there exist multiple possibilities to represent a chemical reaction in a vector representation, or fingerprint, this is not the case for reaction templates. As a consequence, methods to navigate the space of reaction templates are limited. In this work, we introduce the first reaction template fingerprint. To this end, we follow a data-driven approach relying on a masked language modelling task on SMIRKS strings. We combine unsupervised pre-training with fine-tuning on the classification of templates according to the RXNO ontology, for which we achieve up to 98.4% classification accuracy. We highlight how the learned embeddings can be extracted and used in downstream applications.