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.

项目概要 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 键是合成医学相关分子的重要工具，这些反应通常被催化。 过渡金属的络合物，例如钯或铜催化的交叉偶联反应。 与以钯为中介的信用相比有几个优点，但与铜相关的缺点 大多数铜催化的交叉偶联方法需要高负载量。 催化剂和高温，铜催化剂通常不能实现芳基氯的交叉偶联。 为了解决这些问题，增加铜催化剂 C-N 和 C-O 交叉偶联活性的配体已被开发出来。 草酰胺配体已被证明可以产生一些最活跃的 A 系列催化剂。 马云的出版物表明，在某些情况下，此类催化剂可以与 10,000 次翻转发生反应，并且可以 尽管在高温（120°C）和催化剂负载（5-10 mol%）下，芳基氯会发生交叉偶联。 确定促进给定底物对交叉偶联的反应条件可能很困难 – 12 种不同的反应条件 草酰胺配体已被用来以高产率实现不同底物组合的偶联。 在此，我们建议使用机械研究和机器学习（ML）来促进 铜盐与草酰胺配体介导的 C-O 交叉偶联反应的反应条件的鉴定 并促进改进配体和方法的开发。 理解可用于构建改进的 ML 模型，该模型可以使用 100-1000 点数量级的数据集 有效预测反应产率一旦建立，就可以使用能够预测产率的机器学习模型来评估。 计算机模拟配体产生芳基氯交叉偶联催化剂的潜力，或预测 反应条件以实现新的偶联伙伴组合的高产率。 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。 由铜盐与草酰胺配体催化，并确定配体结构如何影响反应 获得的机制见解将用于识别或开发机器学习的输入。 我们将使用高通量实验工具进行 960 C-O 偶联反应。 各种芳基卤化物、亲核试剂和草酰胺配体的数据集将用于比较我们的手工数据。 机器学习算法通过预测 C-O 耦合率的能力来选择特征。 我们将针对三种不同的机器学习优化任务进行比较。 首次揭示了铜盐与草酰胺配体催化的C-O交叉偶联反应的机理， 为使用同一催化剂研究其他C-O和C-N交叉偶联反应奠定基础 我们的机器学习研究将建立能够使用相当小的数据集来预测反应产量的方法。