Feeding Machine Learning Algorithms with Mechanistic Data to Predict Outcomes of Copper-Catalyzed Couplings

项目总结 将芳基卤化物与氧和氮亲核试剂结合形成C-N和C-O的交叉偶联反应 键是合成与医学相关的分子的重要工具。这些反应通常是由 过渡金属络合物，如钯或铜。铜催化的交叉偶联反应 与钯相对应的化合物相比有几个优点，但与铜相关的缺点 催化作用往往超过了这些好处。大多数铜催化的交叉偶联方法需要高负载的 催化剂和高温下，铜催化剂往往不能实现芳基氯化物的交叉偶联。 为了解决这些问题，增加铜催化剂对C-N和C-O交叉偶联活性的配体有 人们一直在寻找草酰胺配体，并且已经证明草酰胺配体可以产生一些最活跃的催化剂。一系列 马云发表的一系列文章表明，在某些情况下，这种催化剂可以与10,000次失误发生反应，并且可以 交叉偶联的芳基氯化物，尽管在高温(120°C)和催化剂负载(5-10mol%)下。然而， 确定促进一对特定底物交叉偶联的反应条件可能很困难-12个不同的 草酰胺配体已被用来实现不同底物组合的高产率偶联。 在这里，我们建议使用机械研究，结合机器学习(ML)，以促进 铜盐与草酰胺配体催化C-O交叉偶联反应条件的确定 并促进改进的配体和方法的开发。我们假设机械论 理解可用于构建改进的ML模型，该模型可以使用100-1000个点的数量级的数据集 为了有效地预测反应产率。一旦建立了能够预测产量的ML模型，就可以用来评估 在电子计算机中，配体产生芳基氯化物交叉偶联催化剂的潜力，或预测 反应条件为获得高产率的新型偶联伙伴组合。 我们的研究策略如下。首先，我们将阐明C-O交叉偶联反应的机理 由含草酰胺配体的铜盐催化，并确定配体结构对反应的影响 机制。获得的机械性见解将用于识别或开发机器学习的输入 模型(功能)。我们将使用高通量实验工具进行960个C-O偶联反应 各种芳基卤化物、亲核试剂和草酰胺配体。数据集将被用来比较我们的手- 所选特征具有由ML算法根据其预测C-O耦合产额的能力选择的特征。这个 将对三种不同的ML优化任务进行比较。我们的机械研究将为 首次研究了铜盐与草酰胺配体催化的C-O交叉偶联反应机理。 为使用同一催化剂的其他C-O和C-N交叉偶联反应的研究奠定了基础 系统。我们的ML研究将建立使合理的小数据集能够预测反应产率的方法。

PROJECT SUMMARY Cross-coupling reactions that combine aryl halides with oxygen and nitrogen nucleophiles to form C-N and C-O bonds are vital tools for the synthesis of medicinally-relevant molecules. These reactions are often catalyzed by complexes of transition metals, such as palladium or copper. Copper-catalyzed cross-coupling reactions have several advantages over their palladium-mediated counterparts, but the disadvantages associated with copper catalysis often outweigh these benefits. Most Cu-catalyzed cross-coupling methods require high loadings of catalyst and high temperatures, and copper catalysts are often unable to effect cross-coupling of aryl chlorides. To address these issues, ligands that increase activity of copper catalysts for C-N and C-O cross-couplings have been sought, and oxalamide ligands have been shown to generate some of the most active catalysts. A series of publications by Ma have shown that such catalysts can, in some cases, react with 10,000 turnovers, and can cross-couple aryl chlorides, albeit at high temperature (120 °C) and loading of catalyst (5-10 mol %). However, identifying reaction conditions to promote cross-coupling of a given pair of substrates can be difficult – 12 different oxalamide ligands have been used to achieve couplings of different combinations of substrates in high yield. Herein, we propose to use mechanistic research, together with machine learning (ML), to facilitate the identification of reaction conditions for C-O cross-coupling reactions mediated by Cu salts with oxalamide ligands and to facilitate the development of improved ligands and methods. We hypothesize that mechanistic understanding can be used to build improved ML models that can use data sets on the order of 100-1000 points to predict reaction yield effectively. Once built, an ML model capable of predicting yield can be used to evaluate in-silico the potential of a ligand to generate a catalyst for the cross-coupling of aryl chlorides, or to predict reaction conditions to achieve high yield for a new combination of coupling partners. Our research strategy is as follows. First, we will elucidate the mechanism of C-O cross-coupling reactions catalyzed by copper salts with oxalamide ligands, and determine how ligand structure influences the reaction mechanism. The mechanistic insights gained will be used to identify or develop input for a machine learning model (features). We will use high-throughput experimentation tools to carry out 960 C-O coupling reactions with a variety of aryl halides, nucleophiles, and oxalamide ligands. The data set will be used to compare our hand- selected features with features selected by a ML algorithm by their ability to predict C-O coupling yield. The comparison will be made across three different ML optimization tasks. Our mechanistic studies will establish for the first time the mechanism of a C-O cross-coupling reaction catalyzed by Cu salts with oxalamide ligands, laying the foundation for the investigation of other C-O and C-N cross-coupling reactions using the same catalyst system. Our ML studies will establish methods that enable reasonably small datasets to predict reaction yield.