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

有机合成是历史上影响最大的科学发展之一，通过在医学、农业和材料方面的突破，极大地提高了生活质量。尽管有这些进步和一个多世纪的研究，但在大多数实验室里，有机合成的实践与20世纪初的做法没有显著的变化。使用在烧瓶中进行的单个实验，通过迭代和经常反复试验的方法来优化单个化学反应。虽然这在过去运作得很好，但我们现在正处于这样一个时刻，该领域的持续进展需要新的、更有效的技术和工具，以使人们能够更深入地了解化学反应。 Leitch团体节目的主题是“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)。具体地说，我们将解决有机化学中一个核心重要但仍未解决的问题：如何以定量的方式预测化学反应活性，并使用这些预测来开发新的和更有效的化学合成？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的机理模型，即目前用于药物合成的关键化学反应的化学反应性地图，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成百上千的化学反应速率和活化能。将这些值与计算的每个化学物种的分子参数相结合，将生成大量、可靠和一致的数据集。这些数据集的大小和机械基础将是通过算法驱动的统计分析建立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的化学反应结果，从而对控制化学反应的因素有更深入和更全面的了解。 这项研究在学术和工业领域的潜在影响都是巨大的。预测特定反应的结果的能力将在追求新的疗法、农用化学品和先进材料方面节省无数的工时。能够定量地绘制出化学结构如何影响反应活性，将使人们能够以合理的方式发现新的、更有效的合成方法。最后，我们的反应性地图将是强大的数据集，可以在此基础上建立用于化学合成设计的预测性人工智能系统；这一方面是该计划的最终目标之一。

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.