Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation

Methods to access chiral sulfur(VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data-science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of the enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.

Nickel-Catalyzed Enantioselective Synthesis of Dienyl Sulfoxide

Sulfoxides are widely used in the pharmaceutical industry and as ligands in asymmetric catalysis. However, the efficient asymmetric synthesis of this structural motif remains limited. In this study, we disclosed a Ni-catalyzed enantioconvergent reaction that utilizes both racemic allenyl carbonates and β-sulfinyl esters. Our method employs cheap and more sustainable Ni(II) as a precatalyst and successfully overcomes the challenging poisoning effect and instability of sulfenate generated in situ. This enables the synthesis of a series of dienyl sulfoxides with enantioselectivity of up to 98 % ee. The product exhibits tremendous potential in various applications, including diastereoselective Diels-Alder reactions, coordination with transition metals, and incorporation into medicinal compounds, among others. Using a combination of experimental and computational methods, we have uncovered an interesting associated outersphere mechanism that contrasts with conventional mechanisms commonly observed in asymmetric transition metal catalysis.

Classification of Hemilabile Ligands Using Machine Learning

Hemilabile ligands have the capacity to partially disengage from a metal center, providing a strategy to balance stability and reactivity in catalysis, but they are not straightforward to identify. We identify ligands in the Cambridge Structural Database that have been crystallized with distinct denticities and are thus identifiable as hemilabile ligands. We implement a semi-supervised learning approach using a label-spreading algorithm to augment a small negative set that is supported by heuristic rules of ligand and metal co-occurrence. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile, and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. Feature importance analysis of our models shows that the second, third, and fourth coordination spheres all play important roles in ligand hemilability.