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March 14, 2026Nature Synthesis3 citationsOpen Access

Anti-Friedel–Crafts alkylation via electron donor–acceptor photoinitiation

DVDavid M. VaheyMMManting MuSBShannon A. Bonke

Key Points

  • To develop a selective, scalable, and transition-metal-free method for C–H alkylation of electron-poor aromatics under mild conditions.
  • Introduced a redox-active phthalimide ester tag.
  • Formed electron donor–acceptor complexes that fragment upon photoexcitation.
  • Conducted mechanistic studies and computational analyses to understand selectivity.
  • Achieved high selectivity for alkylation at the most electrophilic position of electron-deficient aromatics.
  • Demonstrated broad functional group tolerance.
  • Validated selectivity predictions through Fukui indices and machine-learning models.

Abstract

Abstract The ubiquity of C–H bonds in organic molecules makes direct C–H functionalization 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 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 functionalization of pharmaceutical compounds. The mechanistic design exploits a redox-active phthalimide ester tag to form an electron donor–acceptor 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 framework necessary to forecast selectivity in previously ‘unseen’ substrates, thereby enabling selective alkylation of a wide range of complex molecules and late-stage pharmaceuticals.

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Cite This Study

Vahey et al. (2026) studied this question.

synapsesocial.com/papers/69b4fc59b39f7826a300d2b7https://doi.org/10.1038/s44160-026-00994-w
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