Development of a novel photocatalytic system for direct deoxyfunctionalization of alcohols involving machine learning

项目摘要 开发通用、高效的醇类官能化方法具有重要意义。 由于该官能团在天然产物中的普遍存在和突出，因此有必要。 这种方法将允许复杂分子的后期多样化，因此， 可能对天然产物合成和相关药物的制备产生广泛影响。 医药材料。然而，由于醇的化学惰性，大多数方法 通常需要安装用于官能化的活化基团，使得它们 从原子和步骤经济的角度来看，这是没有吸引力的。尽管如此，许多进步 被制造了。特别是，Barton-McCombie反应已成为一种不可或缺的工具， 醇的还原官能化。这种转变需要预先... 醇底物的官能化，采用高毒性锡试剂，并调用使用 较高的反应温度或有害的紫外光引发自由基中间体。 此外，整体转化限于H原子掺入或还原性转化。 与烯烃偶联。最后，只有少数脱氧方法存在，是适合的。 复杂系统中的后期和定点脱氧。此外，物理有机 化学工具可用于促进选择一组条件或参数，以提供 位点选择性是有限的。 在本研究中，我们将开发一种温和且实用的光催化醇类脱氧方法。 我们的策略将集中在解决巴顿麦康比反应的固有局限性1） 避免使用有毒的锡试剂，2）避免了对预官能化的需要， 醇底物，和3）允许形成的烷基通过Ni催化的模块化偶联。 具体目标1探索了一种新型光氧化还原催化的脱氧剂的开发， 酒精。此外，我们还概述了通过以下方式对醇进行脱氧官能化的一般方案： 通过β-断裂形成的烷基自由基中间体的起始， 亲电体此外，我们强调了直接交叉耦合的创新方法， 通过金属光氧化还原催化，以外消旋和对映选择性的方式合成醇。具体 目标2涉及实施物理化学技术的设计战略， 机器学习，以促进反应性能的优化和预测， 多维化学空间此外，我们概述了应用这一战略，以确定一组 最佳条件，以赋予复杂的多元醇中的位点选择性官能化。

Project Summary Development of general and efficient methods for functionalization of alcohols is highly warranted due to the ubiquity and prominence of this functional group in natural products. Such methods would allow for late-stage diversification of complex molecules and, consequently, could have a broad impact in natural product synthesis and preparation of relevant pharmaceutical materials. However, owing to the chemical inertness of alcohols, most methods typically require installation of activating groups for functionalization, making them unattractive from an atom- and step-economical perspective. Nonetheless, many advances have been made. In particular, the Barton-McCombie reaction has become an indispensable tool for reductive functionalization of alcohols. Unfortunately, this transformation requires pre- functionalization of the alcohol substrate, employs highly toxic tin reagents, and invokes the use high reaction temperatures or harmful UV light for initiation of radical intermediates. Furthermore, the overall transformation is limited to H-atom incorporation or reductive coupling with alkenes. Lastly, only a few deoxygenation methods exist that are amenable for late-stage and site-selective deoxygenation in complex systems. Moreover, physical organic chemistry tools available to facilitate the selection of a set of conditions or parameters to afford site-selectivity are limited. In this proposal, we will develop a mild and practical photocatalytic deoxygenation of alcohols. Our strategy will focus on solving the inherent limitation of the Barton McCombie reaction by 1) avoiding the use of toxic tin reagents, 2) obviating the need for pre-functionalization of the alcohol substrate, and 3) allowing for modular coupling of formed alkyl radicals via Ni-catalysis. Specific aim 1 explores the development of a novel photoredox-catalyzed deoxygenation of alcohols. In addition, we outline a general protocol for deoxyfunctionalization of alcohols via inception of the alkyl radical intermediate, formed via β-scission, with various radical electrophiles. Moreover, we highlight an innovative method for the direct cross-coupling of alcohols via metallophotoredox catalysis in both racemic and enantioselective fashion. Specific aim 2 addresses the design strategy for implementing physical chemistry techniques such as Machine Learning in order to facilitate optimization and prediction of reaction performance in multi-dimensional chemical space. Also, we outline applying this strategy to identify a set of optimal conditions to confer site-selective functionalization in complex polyols.