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Peer-reviewedCC BYACS catalysis

Data-driven Investigation of Asymmetric Photo-organocatalyzed Alkylation of Indoles

  1. 1Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, UT 84112, USA.
  2. 2Department of Chemical Sciences, University of Padova, Via Marzolo 1, 35131, Padova, Italy.

Abstract

Enantioselective dual catalysis represents a powerful strategy for the preparation of functionally dense optically active compounds. Photocatalysis, paired with organocatalysis, undeniably represents a popular choice that takes advantage of radical and polar reactivity for crafting challenging bonds. Despite the interest in this methodology, the detailed analysis of the effects governing selectivity between the two catalytic cycles has not been extensively studied. Herein, we report a data-driven mechanistic study campaign to disclose what influences the enantioselectivity of a photo-organocatalytic alkylation of indoles. Data science tools were used to generate a virtual library of potential substrates, thus ensuring the selection of a statistically diverse training set. Subsequent multivariate linear regression (MLR) analysis led to the identification of substrate electronic features as the key factor affecting the stereochemical outcome. Moreover, comprehensive mechanistic experiments unveiled the key role of the in-situ generated oxindole intermediate that connects the two catalytic cycles for the observed selectivity. These findings lay the foundation for developing asymmetric dual catalytic transformations of synthetic relevance and provide a general platform for interrogating other complex catalytic systems.

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BibTeX
@article{lo2026datadriven,
  title = {{Data-driven Investigation of Asymmetric Photo-organocatalyzed Alkylation of Indoles}},
  author = {Raymond Z. Lo and Simone Baldon and Luca Dell’Amico and Matthew S. Sigman and Cristian Rosso},
  journal = {ACS catalysis},
  year = {2026},
  month = oct,
  doi = {10.1021/acscatal.6c06766},
  url = {https://doi.org/10.1021/acscatal.6c06766},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Data-driven Investigation of Asymmetric Photo-organocatalyzed Alkylation of Indoles
AU  - Raymond Z. Lo
AU  - Simone Baldon
AU  - Luca Dell’Amico
AU  - Matthew S. Sigman
AU  - Cristian Rosso
PY  - 2026
DA  - 2026/10/25
JO  - ACS catalysis
DO  - 10.1021/acscatal.6c06766
UR  - https://doi.org/10.1021/acscatal.6c06766
AB  - Enantioselective dual catalysis represents a powerful strategy for the preparation of functionally dense optically active compounds. Photocatalysis, paired with organocatalysis, undeniably represents a popular choice that takes advantage of radical and polar reactivity for crafting challenging bonds. Despite the interest in this methodology, the detailed analysis of the effects governing selectivity between the two catalytic cycles has not been extensively studied. Herein, we report a data-driven mechanistic study campaign to disclose what influences the enantioselectivity of a photo-organocatalytic alkylation of indoles. Data science tools were used to generate a virtual library of potential substrates, thus ensuring the selection of a statistically diverse training set. Subsequent multivariate linear regression (MLR) analysis led to the identification of substrate electronic features as the key factor affecting the stereochemical outcome. Moreover, comprehensive mechanistic experiments unveiled the key role of the in-situ generated oxindole intermediate that connects the two catalytic cycles for the observed selectivity. These findings lay the foundation for developing asymmetric dual catalytic transformations of synthetic relevance and provide a general platform for interrogating other complex catalytic systems.
SN  - 2155-5435
ER  -