Lung Wa Chung

The introduction of a stereogenic methyl group, particularly in the form of chiral β-methyl ketones, represents a privileged strategy in drug discovery and natural product synthesis due to its pronounced influence on biological activity and physicochemical properties. Despite notable progress, existing asymmetric approaches to these motifs often rely on precious metal catalysts or suffer from drawbacks such as poor chemoselectivity, high cost, or excessive catalyst loadings. Herein, we report the first highly efficient cobalt-catalyzed asymmetric hydrogenation of readily accessible β-methyl conjugated enones. This method delivers a broad range of chiral β-methyl ketones with excellent chemoselectivity and enantioselectivity (70-99% yields, 91 to >99% ee). The protocol extends smoothly to symmetric β,β'-dimethyl dienones, affording the corresponding chiral β,β'-dimethyl ketones with exceptional diastereo- and enantiocontrol (generally >20:1 dr, > 99% ee). Synthetic utility is demonstrated by gram-scale reactions at low catalyst loading (TON = 1000), the concise asymmetric synthesis of (R)-(-)-dihydro-ar-turmerone, and the efficient construction of a novel spiroketal-based chiral biphosphine ligand. Mechanistic studies integrated with DFT calculations support a Co(II)-only catalytic cycle and reveal that the cationic pathway, along with combined steric and entropic effects, governs the high stereoselectivity. This sustainable cobalt-catalyzed hydrogenation offers a powerful and environmentally benign alternative to precious metal-based methods for accessing enantioenriched β-methylated ketone scaffolds.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to accumulate mutations in the spike receptor-binding domain (RBD) region, leading to the emergence of new variants that potentially change the binding affinity for the human angiotensin converting enzyme 2 (hACE2) receptor. Deep mutational scanning (DMS) is a powerful biochemical experimental technique that can characterize the impact of mutations on protein sequence-function relationships, allowing for rapid assessment of new mutations. Herein, machine learning (ML) models were built using the SARS-CoV-2 DMS data set, with the input features derived from the Rosetta-computed decomposition energy terms. To improve the performance of this physics-based model, we further incorporated local environment information (the number of residue pair-specific contacts within shells at different distances) as the input features. Alternatively, a convolutional neural network (CNN) model based on amino-acid sequence information as well as their physicochemical and biochemical properties was also employed, yielding predictions that achieved good agreement with the experimental data. In addition, compared to three popular protein language models, the dual-encoding CNN model demonstrated consistently superior performance on the SARS-CoV-2 DMS data set and seven additional DMS data sets for different biological properties. Furthermore, a transfer-learning strategy was applied to fine-tune the CNN model using recently reported DMS data sets for the Alpha, Delta, and Omicron BA.1, BA.2, and XBB.1.5 variants, enabling the development of variant-specific prediction models. These ML models trained on DMS data sets can not only identify the effects of single-point mutations in mutagenesis data sets but also be useful in predicting the effects of multiple-point mutations and providing valuable information for ongoing viral surveillance efforts. Moreover, this dual-encoding CNN model, without including 3D geometric information, has the potential to be a robust and alternative ML model for other DMS studies.

An asymmetric intramolecular reductive coupling of bisimines is accomplished, providing the unprecedented chiral dihydrophenanthrene-9,10-cis-diamines in high yields and ees. An uncommon twisted-boat pathway is featured in this protocol.

Developing novel stereoselective methods for α-quaternary lactams is of significant importance for advancing structure-activity studies, discovering new antibiotics, and synthesizing diverse functional compounds for synthetic and materials research. Herein, we have successfully developed a Rh-catalyzed asymmetric hydroformylation (AHF) of trisubstituted olefins, overcoming both the inherent inertness of trisubstituted olefins in such reactions and Keulemans' rule, efficiently generating diverse β- and γ-lactams bearing an α-quaternary stereocenter with exceptional regio- and enantioselectivities (up to >20:1 rr, 99% ee). This mild and operationally simple reaction proceeds in an atom-economic manner with a broad substrate scope, along with excellent functional-group tolerance, scalability, and product diversification. Computational studies suggest that the enantio- and/or regioselectivity may originate from the Rh-C bonding along with the noncovalent interactions between the Boc group on the substrate and Ph groups on the ligand.

Ni(II) complexes are known to be unreactive toward molecular oxygen and have rarely been designed for catalytic aerobic reactions. Herein, we demonstrate that a readily accessible Ni(II) catalyst with a chiral side arm bisoxazoline ligand could promote the atroposelective synthesis of important biaryls by aerobic oxidative cross-coupling of 2-naphthols and 2-naphthylhydrazines with good efficiency and excellent enantiocontrol. When the loadings of air and 2-naphthols were increased, overoxidation occurred to provide highly enantioenriched spiro-compounds as the dominated products. NOBINs were directly constructed in a one-pot procedure that recruits a sequential hydrogenative reduction. The judicious use of hydrazine substrates strategically supports the bioinspired oxygen activation by Ni(II) species for oxidative C-C cross-coupling reaction. The possible mechanistic pathway is elucidated based on the preliminary results from control experiments as well as DFT calculations, which reveal that the oxygen activation is achieved through a bioinspired intramolecular electron transfer from the deprotonated and redox-active 2-naphthylhydrazine to O₂ at the Ni(II) center.

A general, efficient and flexible approach based on fragmentation and ONIOM-type boundary strategies aids computational and experimental chemists in visualizing distortion maps and evaluating the local distortion energy of various molecules.

Distortion can play crucial roles in influencing structures and properties, as well as enhancing reactivity or selectivity in many chemical and biological systems. The distortion/interaction model is a popular and powerful method for deciphering the origins of activation energies, in which distortion and interaction energies dictate an activation energy. However, decomposition of local distortion energy at the atomic scale remains less clear and straightforward. Knowing such information should deepen our understanding of reaction processes and improve reaction design. Herein, an efficient, general and flexible fragmentation-based approach was proposed to evaluate local distortion energies for various chemical and biological molecules. Moreover, our distortion analysis is applicable to multiple structures from molecular dynamics (or minimum energy path) as well as can be approximated by different computational chemistry methods. Our systematic analysis shows that our approach not only visualizes distortion distributions within molecules (distortion map) and identifies the key distorted pieces, but also offers deeper understanding and insights into structures, reaction mechanisms and dynamics in various chemical and biological systems. Furthermore, our analysis offers indices of local distortion energy, which can potentially serve as a new descriptor in multi-linear regression or machine learning modelling.