%<i>V</i><sub>Bur</sub> index and steric maps: from predictive catalysis to machine learning

Escayola, Sílvia; Bahri-Laleh, Naeimeh; Poater, Albert

doi:10.1039/d3cs00725a

Cited by 32 publications

(8 citation statements)

References 312 publications

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“…While accurate predictions were obtained for the BOP/BAP ligand family without the need for descriptors with explicit chiral information, future studies extending this approach to a wider diversity of ligands might consider the incorporation of steric maps and other comprehensive steric descriptors. 79,80 The coefficients of the ridge regression model allow for the extraction of fundamental chemical insights. Each coefficient represents the weight of a (standardized) descriptor in the linear equation that predicts the enantioselectivity of the Negishi reaction (Figure 7A).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…We note that the LKB-P descriptors used to train the model can be computed for any monodentate phosphine ligand, allowing for the potential future consideration of a wider chemical space of chiral ligands using analogous models. While accurate predictions were obtained for the BOP/BAP ligand family without the need for descriptors with explicit chiral information, future studies extending this approach to a wider diversity of ligands might consider the incorporation of steric maps and other comprehensive steric descriptors. , …”

Section: Resultsmentioning

confidence: 99%

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Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Kelly,

Sreekumar,

Manee

et al. 2024

ACS Catal.

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In this study, we introduce an approach for predicting the enantioselectivity of P-chiral monophosphorus ligands from ligand-based descriptors that can be applied to catalytic systems with small experimental datasets without reliance on mechanistic knowledge. Principal component analysis (PCA) is used to map out the chemical space described by steric and electronic descriptors computed for dihydrobenzooxaphosphole (BOP) and dihydrobenzoazaphosphole (BAP) ligands. The PCA map captures trends in the experimentally measured enantioselectivity of four C−C bond-forming reactions and identifies "hotspots" of selective ligands that provide insight into the optimal balance of sterics and electronics for each reaction. Furthermore, the descriptors are used to train a ridge regression model that quantitatively predicts the enantioselectivity of a Pd-catalyzed Negishi cross-coupling reaction. The coefficients of the model provide fundamental chemical understanding and reveal that a π-stacking interaction with one of the ligands results in an unexpected selectivity inversion. Overall, this integrated approach combines ligand-based descriptors with small experimental datasets to provide qualitative (PCA) and quantitative (ridge regression) enantioselectivity predictions.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Kelly,

Sreekumar,

Manee

et al. 2024

ACS Catal.

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“…Finally, the effect of substituents on the alkyne has been evaluated in a predictive way. , In the case of using propyne instead of acetylene, TS Isom_3 is destabilized (31.4 vs 29.5 kcal/mol with acetylene), and also Λ- cis A results in destabilization (1.7 vs −4.9 kcal/mol) due to the larger steric hindrance. The P–Re–P angle is distorted increasing the steric hindrance of the substituents of the simple acetylene, going from 157.3 to 155.2° for propyne.…”

Section: Resultsmentioning

confidence: 99%

Rhenium Alkyne Catalysis: Sterics Control the Reactivity

Tomasini,

Gimferrer,

Caporaso

et al. 2024

Inorg. Chem.

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Metathesis reactions, including alkane, alkene, and alkyne metatheses, have their origins in the fundamental understanding of chemical reactions and the development of specialized catalysts. These reactions stand as transformative pillars in organic chemistry, providing efficient rearrangement of carbon–carbon bonds and enabling synthetic access to diverse and complex compounds. Their impact spans industries such as petrochemicals, pharmaceuticals, and materials science. In this work, we present a detailed mechanistic study of the Re(V) catalyzed alkyne metathesis through density functional theory calculations. Our findings are in agreement with the experimental evidence from Jia and co-workers and unveil critical factors governing catalyst performance. Our work not only enhances our understanding of alkyne metathesis but also contributes to the broader landscape of catalytic processes, facilitating the design of more efficient and selective transformations in organic synthesis.

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“…Buried volume (abbreviated to %V Bur ) is a descriptor for the visualization of ligand steric hindrance in coordination complexes. [49,50] The corresponding steric maps are shown in Figure 5, and details are provided in the SI. The total %V Bur values reported in Figure 5 correlate with the experimental stereoselectivity of 1-6, demonstrating how small modifications of the occupied space may induce significant changes in the experimental stereoselectivity (Figure S3).…”

Section: Dft Calculation Resultsmentioning

confidence: 99%

Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

Romano,

Barone,

Budzelaar

et al. 2024

Chemistry An Asian Journal

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The stereoelectronic factors responsible for stereoselectivity in propene polymerization with several metallocene and post‐metallocene transition metal catalysts have been revisited using a combined approach of DFT calculations, the Activation Strain Model, Natural Energy Decomposition Analysis and a molecular descriptor (%VBur). There are in most cases two different paths leading to the formation of stereoerrors (SE), and the classical model does not suffice to fully understand stereoregulation. Improving stereoselectivity requires raising the energies of both SE insertion transition states. Our analyses show that the degrees of deformation of the active site (catalyst+chain) and the prochiral monomer differ for these two paths, and between different catalyst classes. Based on such analyses we discuss: a) the subtle differences in SE formation between stereoselective catalysts with different ligand frameworks; b) the reason for exceptional stereoselectivity reported for a special ansa‐metallocene catalyst; c) the (double) stereocontrol origin for isoselective catalysts; d) the electronic contribution for isoselective catalysts generating SE by a modification of the ligand wrapping mode during the polymerization. Although this study will not immediately suggest new catalyst structures, we believe that understanding stereoregulation in great detail will increase our chances of success.

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%V_Bur index and steric maps: from predictive catalysis to machine learning

Abstract: Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules.

Cited by 32 publications

References 312 publications

Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Rhenium Alkyne Catalysis: Sterics Control the Reactivity

Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

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%VBur index and steric maps: from predictive catalysis to machine learning

Abstract: Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules.

Cited by 32 publications

References 312 publications

Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

Rhenium Alkyne Catalysis: Sterics Control the Reactivity

Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

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%V_Bur index and steric maps: from predictive catalysis to machine learning