Chemical Cartography via High-Throughput Experimentation: Predictive Models, Catalyst Development, and New Synthetic Methodology

通过高通量实验进行化学制图：预测模型、催化剂开发和新的合成方法

• 批准号: RGPIN-2019-04985
• 负责人: Leitch, David
• 金额: $ 2.99万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714826
• 关键词: Chemical; Cartography; via; Throughput; Experimentation

Organic synthesis is among the most impactful scientific developments in history, dramatically improving quality of life via breakthroughs in medicine, agriculture, and materials. Despite these advances and more than a century of research, in most labs the practice of organic synthesis is remarkably unchanged from how it was done in the early 1900s. Individual chemical reactions are optimized through an iterative and often trial-and-error approach using single experiments carried out in flasks. While this has worked well in the past, we are now at a point where continued progress in the field requires new, more efficient techniques and tools to enable a deeper understanding of chemical reactivity. The theme of the Leitch group program is “the exploration of uncharted chemical space.” This means finding new points on the map (novel chemical structures), and studying the paths between these points (chemical reactivity). Specifically, we will address a centrally important but still unsolved problem in organic chemistry: how can one predict chemical reactivity in a quantitative manner, and use these predictions to develop new and more efficient chemical syntheses? My group will tackle this problem by combining fundamental physical chemistry principles with modern high-throughput experimental methods and data analytics. We will use this approach to generate quantitative mechanistic models i.e. maps of chemical reactivity for key chemical reactions currently used for pharmaceutical synthesis, and to develop scalable syntheses of novel three-dimensional carbon frameworks that are at the forefront of modern drug discovery research. Critical to this endeavour is the simultaneous measurement of hundreds-to-thousands of chemical reaction rates and activation energies using high-throughput experimentation. Combining these values with computed molecular parameters for each chemical species will generate large, reliable, and consistent data sets. The size and mechanistic foundation of these data sets will be a distinct advantage in building meaningful quantitative models via algorithm-driven statistical analysis. These models will allow us to predict the outcome of a chemical reaction under a variety of hypothetical conditions, leading to a deeper and more holistic understanding of the factors that control chemical reactivity. The potential impact of this research in both academic and industrial contexts is substantial. The ability to predict the outcome of a given reaction will save countless person-hours in the pursuit of new therapeutics, agrochemicals, and advanced materials. Being able to quantitatively map how chemical structure affects reactivity will enable the discovery of new and more efficient syntheses in a rational manner. Finally, our reactivity maps will be powerful data sets on which to build predictive artificial intelligence systems for chemical synthesis design; this facet is one of the ultimate goals of this program.

有机合成是历史上最具影响力的科学发展之一，通过医学，农业和材料的突破显着提高生活质量。尽管有这些进步和超过世纪的研究，在大多数实验室中，有机合成的实践与20世纪初的做法相比没有明显的变化。单个化学反应通过迭代和经常试错的方法进行优化，使用在烧瓶中进行的单个实验。虽然这在过去效果很好，但我们现在正处于一个需要新的、更有效的技术和工具来更深入地了解化学反应性的阶段。 利奇小组计划的主题是“探索未知的化学空间”。这意味着在地图上找到新的点（新的化学结构），并研究这些点之间的路径（化学反应性）。具体来说，我们将解决有机化学中一个重要但尚未解决的问题：如何以定量的方式预测化学反应性，并利用这些预测来开发新的更有效的化学合成？我的团队将通过将基本物理化学原理与现代高通量实验方法和数据分析相结合来解决这个问题。我们将使用这种方法来生成定量机理模型，即目前用于药物合成的关键化学反应的化学反应性图，并开发处于现代药物发现研究前沿的新型三维碳框架的可扩展合成。 这项工作的关键是使用高通量实验同时测量数百至数千个化学反应速率和活化能。将这些值与每种化学物质的计算分子参数相结合，将生成大型，可靠和一致的数据集。这些数据集的规模和机械基础将是通过算法驱动的统计分析构建有意义的定量模型的明显优势。这些模型将使我们能够预测在各种假设条件下化学反应的结果，从而对控制化学反应性的因素有更深入、更全面的了解。 这项研究在学术和工业背景下的潜在影响是巨大的。预测特定反应的结果的能力将在追求新疗法、农用化学品和先进材料方面节省无数的工时。能够定量地绘制化学结构如何影响反应性将能够以合理的方式发现新的和更有效的合成。最后，我们的反应图将是强大的数据集，在此基础上构建用于化学合成设计的预测人工智能系统;这方面是该计划的最终目标之一。