A Data-Driven Approach to the Development and Understanding of Chiroptical Sensors for Alcohols with Remote γ‑Stereocenters

Dynamic
covalent chemistry-based sensors have recently emerged
as powerful tools to rapidly determine the enantiomeric excess of
organic small molecules. While a bevy of sensors have been developed,
those for flexible molecules with stereocenters remote to the functional
group that binds the chiroptical sensor remain scarce. In this study,
we develop an iterative, data-driven workflow to design and analyze
a chiroptical sensor capable of assessing challenging acyclic γ-stereogenic
alcohols. Following sensor optimization, the mechanism of sensing
was probed with a combination of computational parametrization of
the sensor molecules, statistical modeling, and high-level density
functional theory (DFT) calculations. These were used to elucidate
the mechanism of stereochemical recognition and revealed that competing
attractive noncovalent interactions (NCIs) determine the overall performance
of the sensor. It is anticipated that the data-driven workflows developed
herein will be generally applicable to the development and understanding
of dynamic covalent and supramolecular sensors.