Automated Synthesis, Quantum Chemistry and NMR in the Identification of Structurally Related Macrolide Natural Products

自动合成、量子化学和核磁共振在结构相关大环内酯天然产物鉴定中的应用

• 批准号: 2466582
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2466582
• 关键词: Automated; Synthesis; Quantum; Chemistry; NMR

Synthetic organic chemistry has a huge industrial importance and for many years, chemists have been, and are still, constantly in search for more efficient and robust methods for the construction of organic molecules to meet the high demand. Automation is one strand of a digital chemistry future, which, in combination with machine learning and computer assisted retrosynthesis has the potential to dramatically change how we create molecules. Over the last 10 years, the Aggarwal group have created new reactions and strategies based on boron homologations that is now ripe for transferring to an automation platform. We have shown that combining this homologation chemistry with state-of-the-art quantum chemical calculations and NMR spectroscopy provides a synergy that allows us to design, synthesise and elucidate the structure of 3-dimensionally complex natural products, such as baulamycin A. Our goal now is to address the structure of an even more complex molecule, the antifungal marine natural product caylobolide A, and to use the power of automation to aid in its synthesis. Only 5 of the 12 stereocentres of caylobolide A have been assigned in the literature using Mosher's ester analysis, with the remainder not being resolvable by this method. Key structural features of the molecule are 1,3- and 1,5-related hydroxyl groups. We have developed methodology to create any isomer of such motifs, solving a long-standing problem in asymmetric synthesis.We have also established NMR+computation procedures that allow us to probe the stereochemistry of flexible compounds, such as caylobolide A, based on quantum chemical (or machine learning) prediction of NMR parameters such as proton and carbon chemical shifts, 3JHH, 3JCH and nuclear Overhauser enhancements. These work very well for -OH substituted stereocentres that are close to each other, but are less effective when the stereocentres are a long distance apart (as in caylobolide A) and we may need to explore the application of NMR techniques that are sensitive to longer-range interactions, such as residual anisotropic NMR parameters.

合成有机化学具有巨大的工业重要性，多年来，化学家们一直在，并且仍然在不断地寻找更有效和更强大的方法来构建有机分子，以满足高需求。自动化是数字化学未来的一环，与机器学习和计算机辅助逆合成相结合，有可能极大地改变我们创造分子的方式。在过去的10年里，Aggarwal集团在硼同源的基础上创造了新的反应和策略，现在已经成熟，可以转移到自动化平台。我们已经证明，将这种同源化学与最先进的量子化学计算和核磁共振光谱相结合，可以提供一种协同效应，使我们能够设计、合成和阐明三维复杂天然产物的结构，例如鲍拉霉素A。我们现在的目标是研究更复杂的分子--抗真菌海洋天然产物卡洛内酯A的结构，并利用自动化的能力来辅助其合成。Caylobolide A的12个立体中心中只有5个在文献中用Mosher的酯分析进行了归属，其余的不能用这种方法拆分。该分子的主要结构特征是与1，3和1，5相关的羟基。我们开发了创建此类基序的任何异构体的方法学，解决了不对称合成中的一个长期存在的问题。我们还建立了核磁共振+计算程序，使我们能够基于对核磁共振参数(如质子和碳化学位移、3JHH、3JCH和核Overhauser增强)的量子化学(或机器学习)预测来探索柔性化合物的立体化学，如Caylobolide A。这些方法对于相互靠近的-OH取代的立体中心非常有效，但当立体中心相距较远时就不那么有效了(如在Caylobolide A中)，我们可能需要探索对较长范围相互作用敏感的核磁共振技术的应用，例如残留的各向异性核磁共振参数。