In 2020, Schwaller et al. introduced the Molecular Transformer, a sequence-based model for both forward reaction and retrosynthesis prediction using simplified molecular-input lineentry system (SMILES) strings. Their sequence-to-sequence architecture bypasses the need for laborious rule-based methods by treating reaction prediction as a language transla-tion problem, greatly increasing scalability and ease of encoding. When applied to forward reaction prediction, their model achieved remarkable accuracy on several representative data sets. An extension of their Molecular Transformer to retrosynthetic pathway prediction, using a hyper-graph exploration strategy, also achieved impressive performance in a series of test metrics including round-trip accuracy, coverage, class diversity, and Jensen-Shannon divergence. Their predictive model is publicly available on the IBM RXN for Chemistry website 14 and is advertised to chemists and students as a useful tool in pursuits related to organic synthesis. In addition to access through a web-GUI, programmatic access is provided through a RESTful API, and wrappers for this API exist for both Python 15 and Mathematica. Despite the empirical success of sequence-based models for reaction prediction, without an underlying physical or chemical theory there is nothing to prevent spurious chemical