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1. High-Level Data Fusion Enables the Chemoinformatically Guided Discovery of Chiral Disulfonimide Catalysts for Atropselective Iodination of 2-Amino-6-arylpyridines

   https://doi.org/10.1021/jacs.2c08820  

   Rose, Brennan T.; Timmerman, Jacob C.; Bawel, Seth A.; Chin, Steven; Zhang, Haiming; Denmark, Scott E. (December 2022, Journal of the American Chemical Society)

   Machine-learning methods have great potential to accelerate the identification of reaction conditions for chemical transformations. A tool that gives substrate-adaptive conditions for palladium (Pd)–catalyzed carbon-nitrogen (C–N) couplings is presented. The design and construction of this tool required the generation of an experimental dataset that explores a diverse network of reactant pairings across a set of reaction conditions. A large scope of C–N couplings was actively learned by neural network models by using a systematic process to design experiments. The models showed good performance in experimental validation: Ten products were isolated in more than 85% yield from a range of couplings with out-of-sample reactants designed to challenge the models. Importantly, the developed workflow continually improves the prediction capability of the tool as the corpus of data grows. 

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2. Mechanochemical Solvent‐Free Suzuki–Miyaura Cross‐Coupling of Amides via Highly Chemoselective N−C Cleavage

   https://doi.org/10.1002/anie.202114146  

   Zhang, Jin; Zhang, Pei; Shao, Lei; Wang, Ruihong; Ma, Yangmin; Szostak, Michal (December 2021, Angewandte Chemie International Edition)

   Abstract Although cross‐coupling reactions of amides by selective N−C cleavage are one of the most powerful and burgeoning areas in organic synthesis due to the ubiquity of amide bonds, the development of mechanochemical, solid‐state methods remains a major challenge. Herein, we report the first mechanochemical strategy for highly chemoselective, solvent‐free palladium‐catalyzed cross‐coupling of amides by N−C bond activation. The method is conducted in the absence of external heating, for short reaction time and shows excellent chemoselectivity for σ N−C bond activation. The reaction shows excellent functional group tolerance and can be applied to late‐stage functionalization of complex APIs and sequential orthogonal cross‐couplings exploiting double solventless solid‐state methods. The results extend mechanochemical reaction environments to advance the chemical repertoire of N−C bond interconversions to solid‐state environmentally friendly mechanochemical methods. 

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3. Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling

   https://doi.org/10.1021/jacs.3c09554  

   Akana, Michelle E.; Tcyrulnikov, Sergei; Akana-Schneider, Brett D.; Reyes, Giselle P.; Monfette, Sebastien; Sigman, Matthew S.; Hansen, Eric C.; Weix, Daniel J. (January 2024, Journal of the American Chemical Society)

   Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)–C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2’-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parame-terization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational lig-and library, correlation of observed reaction outcomes with features of the ligands, and in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a fivefold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt. 

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4. NMR “Crystallography” for Uniformly ( ¹³ C, ¹⁵ N)‐Labeled Oriented Membrane Proteins

   https://doi.org/10.1002/anie.201915110  

   Awosanya, Emmanuel O.; Lapin, Joel; Nevzorov, Alexander A. (January 2020, Angewandte Chemie International Edition)

   Abstract In oriented‐sample (OS) solid‐state NMR of membrane proteins, the angular‐dependent dipolar couplings and chemical shifts provide a direct input for structure calculations. However, so far only¹H–¹⁵N dipolar couplings and¹⁵N chemical shifts have been routinely assessed in oriented¹⁵N‐labeled samples. The main obstacle for extending this technique to membrane proteins of arbitrary topology has remained in the lack of additional experimental restraints. We have developed a new experimental triple‐resonance NMR technique, which was applied to uniformly doubly (¹⁵N,¹³C)‐labeled Pf1 coat protein in magnetically aligned DMPC/DHPC bicelles. The previously inaccessible¹H_α–¹³C_αdipolar couplings have been measured, which make it possible to determine the torsion angles between the peptide planes without assuming α‐helical structure a priori. The fitting of three angular restraints per peptide plane and filtering by Rosetta scoring functions has yielded a consensus α‐helical transmembrane structure for Pf1 protein. 

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5. Predictive machine learning models trained on experimental datasets for electrochemical nitrogen reduction

   https://doi.org/10.1039/d3dd00151b  

   Rosser, Darik A; Farris, Brianna R; Leonard, Kevin C (April 2024, Digital Discovery)

   Obtaining useful insights from machine learning models trained on experimental datasets collected across different groups to improve the sustainability of chemical processes can be challenging due to the small size and heterogeneity of the dataset. Here we show that shallow learning models such as decision trees and random forest algorithms can be an effective tool for guiding experimental research in the sustainable chemistry field. This study trained four different machine learning algorithms (linear regression, decision tree, random forest, and multilayer perceptron) using different sized datasets containing up to 520 unique reaction conditions for the nitrogen reduction reaction (NRR) on heterogeneous electrocatalysts. Using the catalyst properties and experimental conditions as the features, we determined the ability of each model to regress the ammonia production rate and the faradaic efficiency. We observed that the shallow learning decision tree and random forest models had equal or better predictive power compared to the deep learning multilayer perceptron models and the simple linear regression models. Moreover, decision tree and random forest models enable the extraction of feature importance, which is a powerful tool in guiding experimental research. Analysis of the models showed the complex interaction between the applied potential and catalysts on the effective rate for the NRR. We also suggest some underexplored catalysts–electrolyte combinations to experimental researchers looking to improve both the rate and efficiency of the NRR reaction. 

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