Quantitative structure–reactivity relationships for synthesis planning: The benzhydrylium case

Selective and feasible reactions are top targets in synthesis planning, both of which depend on the reactivity of the molecules involved. Mayr’s approach to quantifying reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevents access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling de facto real-time reactivity predictions. We use the well-understood chemical space of benzhydrylium ions as an example to demonstrate the functionality of our approach and the performance of the resulting quantitative structure–reactivity relationships (QSRRs). Our results suggest that it is straightforward to build low-cost QSRRs that are accurate, interpretable, and transferable to yet unexplored systems within a given scope of application.