Advancements in chemistry and material science hinge on the availability of high-quality chemical reaction data, and the advent of machine learning (ML) for science has highlighted the value that data can bring to chemistry. One important application is in the pharmaceutical industry, where figuring out how to make novel molecules remains a significant bottleneck, causing delays in the "make" step of the "design, make, test" cycle . Making a molecule (product) includes predicting the reaction pathway (retrosynthesis) and suitable reaction conditions (e.g. solvents and reagents), and optimising for one or more outcomes such as reaction yield, selectivity, and conversion. ML is well suited to assist with these tasks, with a range of tools being developed for forward reaction prediction , retrosynthesis , condition prediction , yield prediction , and closed-loop optimisation .