N,N′-Bisoxalamides Enhance the Catalytic Activity in Cu-Catalyzed Coupling of (Hetero)Aryl Bromides with Anilines and Secondary Amines

Synthetic routes to access dicarbonylated aryls and heteroaryls

1,2-Dicarbonyl compounds are privileged functionalities found in natural products, pharmaceuticals, bioactive molecules, and food items, and are important precursors in catalysis, asymmetric synthesis, polymer chemistry and synthesizing functionalized heterocycles. Herein, this comprehensive review focuses on various approaches for synthesizing 1,2-dicarbonylated aryls and heteroaryls in both intermolecular and intramolecular fashion, covering the dicarbonylation of indoles, imidazoheterocycles, indolizines, aminopyrazoles, pyrroloisoquinolines, coumarins, furan, anilines, phenols, anthranils, and benzil synthesis over the last decade (since 2015). Also, the present review highlights the scope and future perspectives of the approach.

Recent synthetic strategies for N-arylation of pyrrolidines: a potential template for biologically active molecules

The chemistry of nitrogen-containing heterocyclic compounds has been a multifaceted area of research for an extended period due to their varied therapeutic and biological significance. N-Aryl pyrrolidine formed by condensation of aryl group with nitrogen atom of pyrrolidine is present in a wide array of compounds. Various significant activities shown by N-arylated pyrrolidine include anti-Alzheimer, antihypoxic, anticancer, plant activator, analgesic effect, and hepatitis C inhibitor. This review summarizes different synthetic approaches, e.g., transition-metal catalyzed and transition-metal-free synthesis, decarboxylation reaction, reductive amination, nucleophilic cyclization, Ullmann-Goldberg amidation, Buchwald-Hartwig reaction, Chan-Evans-Lam coupling, addition to benzyne, multistep reaction, green synthesis, rearrangement reaction, and multicomponent reaction, to afford the derivatives of N-aryl pyrrolidine. It encompasses synthetic strategies documented from 2015 to 2023.

Catalyst Design and Feature Engineering to Improve Selectivity and Reactivity in Two Simultaneous Cross-Coupling Reactions

Highly active catalysts are required in numerous industrial fields; therefore, to minimize costs and development time, catalyst design using machine learning has attracted significant attention. This study focused on a reaction system where two types of cross-coupling reactions, namely, Buchwald-Hartwig type cross-coupling (BHCC) and Suzuki-Miyaura type cross-coupling (SMCC) reactions, occur simultaneously. Constructing a machine-learning model that considers all experimental conditions is essential to accurately predict the product yield for both the BHCC and the SMCC reactions. The objective of this study was to establish explanatory variables x that considered all experimental conditions within the reaction system involving simultaneous cross-couplings and to design catalysts that achieve the target yield and the development of novel reactions. To accomplish this, Bayesian optimization was combined with established variables x to design new catalysts and enhance reaction selectivity. Moreover, the catalyst design in this study successfully pioneered new reactions involving Cu, Rh, and Pt catalysts in a reaction system that did not previously react with transition metals other than Ni or Pd.

Copper-Catalyzed Reactions of Aryl Halides with N-nucleophiles and Their Possible Application for Degradation of Halogenated Aromatic Contaminants

This review summarizes recent applications of copper or copper-based compounds as a nonprecious metal catalyst in N-nucleophiles-based dehalogenation (DH) reactions of halogenated aromatic compounds (Ar-Xs). Cu-catalyzed DH enables the production of corresponding nonhalogenated aromatic products (Ar-Nu), which are much more biodegradable and can be mineralized during aerobic wastewater treatment or which are principally further applicable. Based on available knowledge, the developed Cu-based DH methods enable the utilization of amines for effective cleavage of aryl-halogen bonds in organic solvents or even in an aqueous solution.

Oxalamide/Amide Ligands: Enhanced and Copper-Catalyzed C-N Cross-Coupling for Triarylamine Synthesis

Triarylamines are privileged core structures that are found in versatile optoelectronic materials. New methods are constantly being sought for their preparation. Herein, a new protocol for triarylamine synthesis is presented where a wide range of diarylamines couple smoothly with aryl bromides mediated by a copper oxalamide (or amide) catalytic system. Notably, a new non-C₂-symmetric 1-isoquinolinamide-based N,N-/N,O-bidentate ligand was introduced that could tolerate bulky diarylamines. Plenty of known optoelectronic functional molecules could be synthesized in good to excellent yields. The practicality of this C-N cross-coupling was illustrated by the gram-scale synthesis of a patented thermally activated delayed fluorescence emitter for organic light-emitting diodes.

Palladium-catalyzed Lewis acid-regulated cascade annulation of alkynes with unactivated alkenes to access diverse α-methylene-γ-lactones

A palladium-catalyzed Lewis acid-regulated cascade annulation of alkynes with unactivated alkenes for the preparation of alkyl substituted α-methylene-γ-lactones with excellent Z/E selectivities was accomplished.

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

To improve product yields in synthetic reactions, it is important to use appropriate catalysts. In this study, we used machine learning to design catalysts for a reaction system in which both Buchwald-Hartwig-type and Suzuki-Miyaura-type cross-coupling reactions proceed simultaneously. First, using an existing dataset, yield prediction models were constructed with machine learning between experimental conditions, including the substrate and catalyst and the yields of the two products. Seven methods for calculating both the substrate and catalyst descriptors were proposed, and the predictive ability of the yield prediction models was discussed in terms of the descriptors and machine learning methods. Then, the constructed models were used to predict the compound yields for new combinations of substrates and catalysts, and the predictions were experimentally validated with high reproducibility, confirming that machine learning can predict yields from experimental conditions with high accuracy. In addition, to design catalysts that will improve the yields in our dataset, we added datasets collected from scientific papers and designed catalyst ligands. The proposed catalyst candidates were tested in actual synthetic experiments, and the experimental results exceeded the existing yields.

Copper-catalyzed sulfonylation of N-tosylhydrazones followed by a one-pot C-N bond formation

A new methodology to synthesize sulfonyl-N-phenylaniline derivatives via the trapping of bromo-sulfone derivatives generated from N-tosylhydrazones (NTHs) with amines is described. The reaction proved successful for a wide range of NTHs and amines and tolerated various functional groups on either coupling partner (35 examples). The mechanism was studied, and we showed that the sulfone formation does not follow a radical pathway.