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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. 

Abstract Catalysis ofO‐atom transfer (OAT) reactions is a characteristic of both natural (enzymatic) and synthetic molybdenum‐oxo and ‐peroxo complexes. These reactions can employ a variety of terminal oxidants, e. g. DMSO,N‐oxides, and peroxides, etc., but rarely molecular oxygen. Here we demonstrate the ability of a set of Schiff‐base‐MoO₂complexes (cy‐salen)MoO₂(cy‐salen=N,N’‐cyclohexyl‐1,2‐bis‐salicylimine) to catalyze the aerobic oxidation of PPh₃. We also report the results of a DFT computational investigation of the catalytic pathway, including the identification of energetically accessible intermediates and transition states, for the aerobic oxidation of PMe₃. Starting from the dioxo species, (cy‐salen)Mo(VI)O₂(1), key reaction steps include: 1) associative addition of PMe₃to an oxo‐O to give LMo(IV)(O)(OPMe₃) (2); 2) OPMe₃dissociation from2to produce mono‐oxo complex (cy‐salen)Mo(IV)O (3); 3) stepwise O₂association with3via superoxo species (cy‐salen)Mo(V)(O)(η¹‐O₂) (4) to form the oxo‐peroxo intermediate (cy‐salen)Mo(VI)(O)(η²‐O₂) (5); 4) theO‐transfer reaction of PMe₃with oxo‐peroxo species5at the oxo‐group, rather than the peroxo unit leading, after OPMe₃dissociation, to a monoperoxo species, (cy‐salen)Mo(IV)(η²‐O₂) (7); and 5) regeneration of the dioxo complex (cy‐salen)Mo(VI)O₂(1) from the monoperoxo triplet³7or singlet¹7by a concerted, asynchronous electronic isomerization. An alternative pathway for recycling of the oxo‐peroxo species5to the dioxo‐Mo1via a bimetallic peroxo complex LMo(O)‐O−O‐Mo(O)L8is determined to be energetically viable, but is unlikely to be competitive with the primary pathway for aerobic phosphine oxidation catalyzed by1.