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Dataset for article Anti-Friedel-Crafts alkylation via electron donor-acceptor photo-initiated radical anion propagation

Title: Dataset for article Anti-Friedel-Crafts alkylation via electron donor-acceptor photo-initiated radical anion propagation
Authors: Vahey, David M; Mu, Manting; Bonke, Shannon; Sommer, Timo; Vangal, Prithvi; Mallia, Carl; García-Melchor, Max; Reisner, Erwin
Publication Year: 2025
Collection: Apollo - University of Cambridge Repository
Subject Terms: electron donor-acceptor; machine learning; organic chemistry; photochemistry; radical
Description: The ubiquity of C–H bonds in organic molecules makes direct C–H functionalisation an atom- and step-efficient strategy in synthetic chemistry. However, direct C–H alkylation, particularly of electron-poor aromatic substrates, remains a major challenge because current methods suffer from limited selectivity; functional group tolerance and/or require harsh acidic, pyrophoric or toxic reagents. Here, we introduce a highly selective, scalable, and transition metal-free synthetic strategy for C–H alkylation of electron-poor aromatics under mild conditions, which also exhibits high functional group tolerance applicable to the late-stage functionalisation of pharmaceutical compounds. The novel mechanistic design exploits a redox active phthalimide ester tag to form an electron donor-acceptor (EDA) complex that fragments upon photoexcitation to yield a nucleophilic alkyl radical, which selectively alkylates the most electrophilic position of electron-deficient aromatics, thereby exhibiting ‘anti-Friedel-Crafts’ selectivity. Mechanistic studies, microkinetic modelling simulations and computational analyses indicate that the reaction then propagates via radical anion autocatalysis. The ‘anti-Friedel-Crafts’ selectivity is consistent with theoretical predictions from Fukui indices and machine learning models that provide the predictive framework necessary to predict selectivity in previously ‘unseen’ substrates, and thereby enable selective alkylation of a wide range of complex molecules and late-stage pharmaceuticals. Manuscript - contains Table 1 and Figure 1–6: schematics, UV−vis, UPS, kinetic studies, cyclic voltammetry, DFT, yields, and photo (as TIF, PDF, XLSX files) Supporting Information (SI) - contains Figures S1–S8: NMR characterisation (1H, 13C, 19F), IR, LC-MS, actinometry, cyclic voltammetry, DFT, machine learning methods (as TIF, PDF, XLSX, ZIP files)
Document Type: dataset
File Description: Text reader, Excel, image viewer; application/zip
Language: English
Relation: https://www.repository.cam.ac.uk/handle/1810/392199; https://doi.org/10.17863/CAM.122984
DOI: 10.17863/CAM.122984
Availability: https://www.repository.cam.ac.uk/handle/1810/392199; https://doi.org/10.17863/CAM.122984
Rights: Attribution 4.0 International (CC BY 4.0) ; https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.D5FC7A49
Database: BASE