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The RXN for Chemistry project, initiated by IBM Research Europe – Zurich in 2017, aimed to develop a series of digital assets using machine learning techniques to promote the use of data-driven methodologies in synthetic organic chemistry. This research adopts an innovative concept by treating chemical reaction data as language records, treating the prediction of a synthetic organic chemistry reaction as a translation task between precursor and product languages. Over the years, the IBM Research team has successfully developed language models for various applications including forward reaction prediction, retrosynthesis, reaction classification, atom-mapping, procedure extraction from text, inference of experimental protocols and its use in programming commercial automation hardware to implement an autonomous chemical laboratory. Furthermore, the project has recently incorporated biochemical data in training models for greener and more sustainable chemical reactions. The remarkable ease of constructing prediction models and continually enhancing them through data augmentation with minimal human intervention has led to the widespread adoption of language model technologies, facilitating the digitalization of chemistry in diverse industrial sectors such as pharmaceuticals and chemical manufacturing. This manuscript provides a concise overview of the scientific components that contributed to the prestigious Sandmeyer Award in 2022

As of now, only rule-based systems support retrosynthetic planning using biocatalysis, while initial data-driven approaches are limited to forward predictions. Here, the authors extend the data-driven forward reaction as well as retrosynthetic pathway prediction models based on the Molecular Transformer architecture to biocatalysis.

Aldol reactions belong to the most important methods for carbon–carbon bond formation and are also involved in one of the most astonishing biosynthetic processes: the biosynthesis of polyketides governed by an extraordinarily sophisticated enzymatic machinery. In contrast to the typical linear or convergent strategies followed in chemical synthesis, this late-stage catalysis concept allows Nature to assemble intermediates that are diversified into a broad range of scaffolds, which assume various crucial biological functions. To transfer this concept to small-molecule catalysis to access products beyond the natural systems, a stepwise approach to differentiate increasingly complex substrates was followed by investigating arene-forming polyketide cyclizations. An outline of our efforts to develop and apply these concepts are presented herein.