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

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $65,994

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10314079
- **Project number:** 1F32GM140550-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Connor Delaney
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $65,994
- **Award type:** 1
- **Project period:** 2021-09-20 → 2024-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10314079

## Citation

> US National Institutes of Health, RePORTER application 10314079, Feeding Machine Learning Algorithms with Mechanistic Data to Predict Outcomes of Copper-Catalyzed Couplings (1F32GM140550-01A1). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/10314079. Licensed CC0.

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