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

Jordan J Dotson,Eric V Anslyn,Matthew S Sigman

doi:10.1021/jacs.1c09443

Jordan J Dotson, Eric V Anslyn + Show 1 more

Open Access

https://doi.org/10.1021/jacs.1c09443

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Abstract

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.

Highlights

The development of dynamic covalent chemistry (DCC) sensors can fundamentally impact the disparate fields of biology, medicine, and materials science.[1,2,3,4] At the heart of successful DCC-based sensors is a robust molecular recognition strategy that involves the binding of and the chemical or conformational response to the analyte
The mechanism of sensing was probed with a combination of computational parameterization of the sensor molecules, statistical modeling, and high-level density functional theory (DFT) calculations
The geometry of each substrate was minimized and 1,357 computationally inexpensive 2D and 3D Quantitative Structure Activity Relationship (QSAR) parameters - simple molecular descriptors routinely used in medicinal chemistry - were calculated for each boronic acid (Step 2, see SI for details).[19]

Summary

Introduction

The development of dynamic covalent chemistry (DCC) sensors can fundamentally impact the disparate fields of biology, medicine, and materials science.[1,2,3,4] At the heart of successful DCC-based sensors is a robust molecular recognition strategy that involves the binding of and the chemical or conformational response to the analyte.

Results

Conclusion