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

将机械数据输入机器学习算法来预测铜催化耦合的结果

• 批准号: 10314079
• 负责人: Connor Delaney
• 金额: $ 6.6万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10314079
• 关键词: Address; Adoption; Affinity; Air; Area; Binding; Bromides; Catalysis; Chlorides; Communities; Complex; Copper; Coupling; Data; Data Set; Development; Disadvantaged; Equation; Fostering; Foundations; Generations; Hand; High temperature of physical object; Informatics; Investigation; Iodides; Ligands; Machine Learning; Mediating; Methods; Modeling; Nitrogen; Organic Chemistry; Oxygen; Palladium; Phosphines; Price; Publications; Reaction; Research; Rest; Salts; Series; Structure; System; Temperature; Testing; Time; Training; Transition Elements; Validation; Work; aryl halide; catalyst; chemical reaction; cost; design; feeding; improved; in silico; insight; large datasets; machine learning algorithm; outcome prediction; predictive modeling; reaction rate; tool

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.

项目摘要 将芳基卤化物与氧和氮核阳光液结合起来形成C-N和C-O的交叉偶联反应 键是与药物相关的分子合成的重要工具。这些反应通常被 过渡金属的络合物，例如钯或铜。铜催化的交叉偶联反应具有 与钯介导的对应物相比，有几个优点，但与铜有关的灾难 催化通常超过这些好处。大多数Cu催化的交叉耦合方法都需要高负载 催化剂和高温以及铜催化剂通常无法实现芳基氯化物的交叉偶联。 为了解决这些问题，增加了C-N和C-O交叉偶联铜催化剂活性的配体 我们受到了寻找，已经证明草氨酰胺配体会产生一些最活跃的催化剂。系列 MA的出版物表明，在某些情况下，这种催化剂可以反应10,000个失误，并且可以 交叉偶芳基氯化物，尽管在高温下（120°C）和催化剂（5-10 mol％）的负载。然而， 识别反应条件以促进给定底物的交叉偶联可能很困难 - 12个不同 草氨酰胺配体已被用来实现高产量的底物不同组合的耦合。 本文中，我们建议将机械研究与机器学习（ML）一起使用，以促进 鉴定由与草氨酰胺配体的Cu沙拉介导的C-O交叉偶联反应的反应条件 并促进改进的配体和方法的发展。我们假设这种机械 理解可用于构建改进的ML模型，该模型可以使用100-1000点的数据集 有效预测反应的屈服。建造后，可以使用能够预测产量的ML模型来评估 在硅胶中，配体产生用于芳基氯化物交叉偶联的催化剂的潜力，或预测 反应条件可实现高收率的新耦合伙伴组合。 我们的研究策略如下。首先，我们将阐明C-O交叉偶联反应的机制 用草氨酰胺配体催化铜盐，并确定配体结构如何影响反应 机制。获得的机械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与960个C-O耦合反应 多种芳基卤化物，核phorides和草氨酰胺配体。数据集将用于比较我们的手 - 选定的功能具有由ML算法选择的功能，其功能通过预测C-O耦合产率的能力。这 比较将在三个不同的ML优化任务中进行。我们的机械研究将建立 第一次由Cu沙拉与草氨酰胺配体催化的C-O交叉偶联反应的机制， 使用相同的催化剂为其他C-O和C-N交叉偶联反应的投资奠定基础 系统。我们的ML研究将建立能够相当小的数据集预测反应产量的方法。