Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States.
Department of Chemistry, University of Texas at Austin, Austin, Texas 78712, United States.
J Am Chem Soc. 2021 Nov 17;143(45):19187-19198. doi: 10.1021/jacs.1c09443. Epub 2021 Nov 4.
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.
基于动态共价化学的传感器最近已成为快速确定有机小分子对映过量的有力工具。虽然已经开发出了许多传感器，但对于具有立体中心且远离与手性光学传感器结合的官能团的柔性分子的传感器仍然很少。在这项研究中，我们开发了一种迭代的、数据驱动的工作流程来设计和分析一种手性光学传感器，该传感器能够评估具有挑战性的非循环γ-立体醇。在传感器优化之后，通过对传感器分子的计算参数化、统计建模和高级密度泛函理论（DFT）计算的组合来探测传感机制。这些被用来阐明立体化学识别的机制，并揭示了竞争的吸引力非共价相互作用（NCIs）决定了传感器的整体性能。预计本文中开发的数据驱动工作流程将普遍适用于动态共价和超分子传感器的开发和理解。
