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

开发一种涉及机器学习的新型光催化系统，用于醇的直接脱氧功能化

• 批准号: 9759306
• 负责人: Marvin Parasram
• 金额: $ 6.12万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2020-07-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759306
• 关键词: Address; Alcohol consumption; Alcohols; Alkenes; Arts; Catalysis; Chemicals; Complex; Coupling; Development; Employment; Intercept; Machine Learning; Methods; Natural Products; Organic Chemistry; Organic Synthesis; Performance; Pharmacologic Substance; Phosphines; Physical Chemistry; Preparation; Protocols documentation; Reaction; Reagent; Site; System; Techniques; Temperature; Tin; Ultraviolet Rays; Visible Radiation; alcohol involvement; catalyst; design; functional group; innovation; novel; polyol; predictive tools; tool

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.

项目概要 开发通用且有效的醇官能化方法非常重要 由于该官能团在天然产物中普遍存在且突出，因此有保证。 这些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 然而，由于醇的化学惰性，大多数方法都采用这种方法。 通常需要安装激活基团进行功能​​化，使它们 然而，从原子经济和步骤经济的角度来看，这并没有什么吸引力。 特别是，Barton-McCombie 反应已成为不可或缺的工具。 不幸的是，这种转化需要预先进行。 醇底物的功能化，采用剧毒的锡试剂，并调用 高反应温度或有害的紫外线会引发自由基中间体。 此外，整体转化仅限于H原子掺入或还原 最后，只有少数适​​合的脱氧方法。 复杂系统中的后期和位点选择性脱氧。 化学工具可用于促进选择一组条件或参数以提供 位点选择性有限。 在本提案中，我们将开发一种温和实用的醇光催化脱氧方法。 我们的策略将重点解决 Barton McCombie 反应的固有局限性：1) 避免使用有毒的锡试剂，2）消除了对预功能化的需要 醇底物，3) 允许通过镍催化对形成的烷基进行模块化偶联。 具体目标 1 探索新型光氧化还原催化脱氧的开发 此外，我们概述了醇脱氧功能化的通用方案。 烷基自由基中间体的开始，通过β-断裂形成，具有各种自由基 此外，我们强调了一种直接交叉偶联的创新方法。 通过金属光氧化还原催化以种族和对映选择性方式生成醇。 目标 2 提出了实施物理化学技术的设计策略，例如 机器学习，以促进反应性能的优化和预测 此外，我们概述了应用该策略来识别一组 在复杂多元醇中赋予位点选择性官能化的最佳条件。