OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

Gallarati, Simone; Gerwen, Puck van; Laplaza, Rubén; Vela, Sergi; Fabrizio, Alberto; Corminbœuf, Clémence

doi:10.1039/d2sc04251g

Cited by 24 publications

(32 citation statements)

References 167 publications

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“…While a large scope for catalyst modularity can be construed as a possible limitation, it is evident that catalyst optimization requires some level of structural feature tuning. Indeed, H-bonding catalysts like thioureas and cinchona alkaloids have multiple points for introducing a broad set of groups encompassing different steric and electronic properties . In contrast, secondary amines have witnessed significantly less structural diversity at the chiral framework.…”

Section: Resultsmentioning

confidence: 96%

“…Indeed, Hbonding catalysts like thioureas and cinchona alkaloids have multiple points for introducing a broad set of groups encompassing different steric and electronic properties. 27 In contrast, secondary amines have witnessed significantly less structural diversity at the chiral framework. This can be demonstrated by the number of unique catalysts present in the database that correspond to a particular chemotype.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

et al. 2023

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The development of chiral catalysts that can provide high enantioselectivities across a wide assortment of substrates or reaction range is a priority for many catalyst design efforts. While several approaches are available to aid in the identification of general catalyst systems, there is currently no simple procedure for directly measuring how general a given catalyst could be. Herein, we present a catalyst-agnostic workflow centered on unsupervised machine learning that enables the rapid assessment and quantification of catalyst generality. The workflow uses curated literature data sets and reaction descriptors to visualize and cluster chemical space coverage. This reaction network can then be applied to derive a catalyst generality metric through designer equations and interfaced with other regression techniques for general catalyst prediction. As validating case studies, we have successfully applied this method to identify-through-quantification the most general catalyst chemotype for an organocatalytic asymmetric Mannich reaction and predicted the most general chiral phosphoric acid catalyst for the addition of nucleophiles to imines. The mechanistic basis for catalyst generality can then be gleaned from the calculated values by deconstructing the contributions of chemical space and enantiomeric excess to the overall result. Finally, our generality techniques permitted the development of mechanistically informative catalyst screening sets that allow experimentalists to rationally select catalysts that have the highest probability of achieving a good result in the first round of reaction development. Overall, our findings represent a framework for interrogating catalyst generality, and this strategy should be relevant to other catalytic systems widely applied in asymmetric synthesis.

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Section: Resultsmentioning

confidence: 96%

Section: ■ Results and Discussionmentioning

confidence: 99%

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

et al. 2023

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“…32,33 While the latter type of descriptor is readily calculable with cheminformatics packages in milliseconds, the computational cost of deriving descriptors from DFT calculations can be significant and render these workflows inaccessible or impractical for many researchers. Efforts like kraken 34 and OSCAR 35 seek to precompute and/or predict descriptor values for hundreds, thousands, or even millions of hypothetical ligands or catalysts.…”

Section: As An Output Variablementioning

confidence: 99%

Dataset Design for Building Models of Chemical Reactivity

Raghavan,

Haas,

Ruos

et al. 2023

ACS Cent. Sci.

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“…A high-throughput computational workow begins populating the database with DFT computational data. Similar data structures involving high-throughput computation for pconjugated organic molecules exist, [25][26][27][28][29][30] and there also exist databases targeting batteries. 19,31,32 But unlike these existing databases, D 3 TaLES provides a data infrastructure and framework for multiple data types targeting NARFB.…”

Section: Introductionmentioning

confidence: 99%

Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries

Duke¹,

Bhat²,

Sornberger³

et al. 2023

Digital Discovery

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OSCAR: an extensive repository of chemically and functionally diverse organocatalysts

Abstract: The automated construction of datasets has become increasingly relevant in computational chemistry. While transition-metal catalysis has greatly benefitted from bottom-up or top-down strategies for the curation of organometallic complexes libraries,...

Cited by 24 publications

References 167 publications

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis

Dataset Design for Building Models of Chemical Reactivity

Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries

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