Which phosphines squeeze together? Phosphine ligands coordinated to palladium and nickel are essential tools for assembling the backbones of pharmaceutical compounds. For decades, descriptors that characterize spatial bulk have helped to guide phosphine optimization. However, these descriptors tend to apply to ideal geometries of a single ligand. Newman-Stonebraker et al . introduce a descriptor that considers how the ligand conformation might change in a crowded environment. Specifically, they found that the minimum percentage buried volume accurately predicts when one or two of a particular ligand will coordinate to a metal center, frequently a key determinant of successful catalysis. —JSY

We report the development of a method to diastereoselectively access tetrasubstituted alkenes via nickel-catalyzed Suzuki-Miyaura cross-couplings of enol tosylates and boronic acid esters. Either diastereomeric product was selectively accessed from a mixture of enol tosylate starting material diastereomers in a convergent reaction by judicious choice of the ligand and reaction conditions. A similar protocol also enabled a divergent synthesis of each product isomer from diastereomerically pure enol tosylates. Notably, high-throughput optimization of the monophosphine ligands was guided by chemical space analysis of the kraken library to ensure a diverse selection of ligands was examined. Stereoelectronic analysis of the results provided insight into the requirements for reactive and selective ligands in this transformation. The synthetic utility of the optimized catalytic system was then probed in the stereoselective synthesis of various tetrasubstituted alkenes, with yields up to 94% and diastereomeric ratios up to 99:1 Z/E and 93:7 E/Z observed. Moreover, a detailed computational analysis and experimental mechanistic studies provided key insights into the nature of the underlying isomerization process impacting selectivity in the cross-coupling. ASSOCIATED CONTENTThe Supporting Information is available free of charge at http://pubs.acs.org." Experimental procedures, characterization and spectral data for all new compounds.

In reaction discovery, the search space of discrete reaction parameters such as catalyst structure is often not explored systematically. We have developed a tool set to aid the search of optimal catalysts in the context of phosphine ligands. A virtual library, kraken, which is representative of the monodentate P­(III)-ligand chemical space, was utilized as the basis to represent the discrete ligands as continuous variables. Using dimensionality reduction and clustering techniques, we suggested a Phosphine Optimization Screening Set (PHOSS) of 32 commercially available ligands that samples this chemical space completely and evenly. We present the application of this screening set in the identification of active catalysts for various cross-coupling reactions and show how well-distributed sampling of the chemical space facilitates identification of active catalysts. Furthermore, we demonstrate how proximity in ligand space can be a useful guide to further explore ligands when very few active catalysts are known.

Statistical analysis of reaction data with molecular descriptors can enable chemists to identify reactivity cliffs that result from a mechanistic dependence on a specific structural feature. In this study, we develop a broadly applicable and quantitative classification workflow that identifies reactivity cliffs in eleven Ni- and Pd-catalyzed cross-coupling datasets employing monodentate phosphine ligands. A unique ligand steric descriptor, %<i>V</i>_bur (<i>min</i>), is found to divide these datasets into active and inactive regions at a similar threshold value. Organometallic studies demonstrate that this threshold corresponds to the binary outcome of bisligated versus monoligated metal and that %<i>V</i>_bur (<i>min</i>) is a physically meaningful and predictive representation of ligand structure in catalysis. Taken together, we expect that this strategy will be of broad value in mechanistic investigation of structure-reactivity relationships, while providing a means to rationally partition datasets for data-driven modeling.

In reaction discovery, the search space of discrete reaction parameters such as catalyst structure is often not explored systematically. We have developed a tool set to aid the search of optimal catalysts in the context of phosphine ligands. A virtual library, kraken, that is representative of the monodentate P(III)-ligand chemical space was utilized as the basis to represent the discrete ligands as continuous variables. Using dimensionality reduction and clustering techniques, we suggested a Phosphine Optimization Screening Set (PHOSS) of 32 commercially available ligands that samples this chemical space completely and evenly. We present the application of this screening set in the identification of active catalyst for various cross-coupling reactions and how well-distributed sampling of the chemical space facilitates identification of active catalysts. Furthermore, we demonstrate how proximity in ligand space can be a useful guide to further explore ligands when very few active catalysts are known.