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Given the prevalence of nitrogen-containing heterocycles in commercial drugs, selectively incorporating a single nitrogen atom is a promising scaffold hopping approach to enhance chemical diversity in drug discovery libraries. We harness the distinct reactivity of sulfenylnitrenes, which insert a single nitrogen atom to transform readily available pyrroles, indoles, and imidazoles into synthetically challenging pyrimidines, quinazolines, and triazines, respectively. Our additive-free method for skeletal editing employs easily accessible, benchtop-stable sulfenylnitrene precursors over a broad temperature range (−30 to 150°C). This approach is compatible with diverse functional groups, including oxidation-sensitive functionalities such as phenols and thioethers, and has been applied to various natural products, amino acids, and pharmaceuticals. Furthermore, we have conducted mechanistic studies and explored regioselectivity outcomes through density functional theory calculations. 

1,2-cis-Furanosides are present in various biomedically relevant glycosides, and their stereoselective synthesis remains a significant challenge. In this vein, we have developed a stereoselective approach to 1,2-cis-furanosylations using earth-abundant copper catalysis. This protocol proceeds under mild conditions at room temperature and employs readily accessible benchtop stable enynalderived furanose donors. This chemistry accommodates a variety of alcohols, including primary, secondary, and tertiary, as well as mannosyl alcohol acceptors, which have been incompatible with most known methods of furanosylation. The resulting 1,2-cisfuranoside products exhibit high yields and anomeric selectivity with both the ribose and arabinose series. Furthermore, the anomeric selectivity is independent of the C2 oxygen-protecting group and the anomeric configuration of the starting donor. Experimental evidence and computational studies support our hypothesis that copper chelation between the C2 oxygen of the furanose donor and an incoming alcohol nucleophile is responsible for the observed 1,2-cisstereoselectivity. 

Abstract In the last several years, there has been a surge in the development of machine learning potential (MLP) models for describing molecular systems. We are interested in a particular area of this field — the training of system‐specific MLPs for reactive systems — with the goal of using these MLPs to accelerate free energy simulations of chemical and enzyme reactions. To help new members in our labs become familiar with the basic techniques, we have put together a self‐guided Colab tutorial (https://cc-ats.github.io/mlp_tutorial/), which we expect to be also useful to other young researchers in the community. Our tutorial begins with the introduction of simple feedforward neural network (FNN) and kernel‐based (using Gaussian process regression, GPR) models by fitting the two‐dimensional Müller‐Brown potential. Subsequently, two simple descriptors are presented for extracting features of molecular systems: symmetry functions (including the ANI variant) and embedding neural networks (such as DeepPot‐SE). Lastly, these features will be fed into FNN and GPR models to reproduce the energies and forces for the molecular configurations in a Claisen rearrangement reaction.