Automated Quantum Chemistry for Estimating Nucleophilicity and Electrophilicity with Applications to Retrosynthesis and Covalent Inhibitors

Ree, Nicolai; Göller, Andreas H.; Jensen, Jan H.

doi:10.26434/chemrxiv-2023-pd395

Cited by 1 publication

(1 citation statement)

References 20 publications

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“…For instance, previous data-driven studies have mostly relied on quantum molecular properties (QMPs) as descriptors, − meaning that each prediction is preceded by quantum chemical (mainly DFT) calculations, which occupy almost 100% of the overall prediction time. This applies less to semiempirical electronic-structure methods, as recently applied in a related context, which can much more efficiently generate QMPs and other electronic descriptors.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Structure–Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case

Eckhoff,

Diedrich,

Mücke

et al. 2023

J. Phys. Chem. A

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Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical 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 prevent 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 realtime 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 QSRR models that are accurate, interpretable, and transferable to unexplored systems within a given scope of application. Moreover, our QSRR approach suggests that Hammett σ parameters are only approximately additive.

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