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

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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.