Generality-Oriented Optimization of Enantioselective Aminoxyl Radical Catalysis

Rein, Jonas; Rozema, Soren D.; Langner, Olivia; Zacate, Samson B.; Hardy, Melissa; Siu, Juno C.; Mercado, Brandon Q.; Sigman, Matthew S.; Miller, Scott C.; Lin, Song

doi:10.26434/chemrxiv-2022-8cd9w-v2

Cited by 5 publications

(6 citation statements)

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“…2C). These results showcase the efficacy of active learning to enhance dataset representation (29)(30)(31).…”

Section: Training Set Curationmentioning

confidence: 61%

Predicting success in Cu-catalyzed C–N coupling reactions using data science

Samha,

Karas,

Vogt

et al. 2024

Sci. Adv.

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Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C–N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

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“…2C). These results showcase the efficacy of active learning to enhance dataset representation (29)(30)(31).…”

Section: Training Set Curationmentioning

confidence: 61%

Predicting success in Cu-catalyzed C–N coupling reactions using data science

Samha,

Karas,

Vogt

et al. 2024

Sci. Adv.

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“…Analyzing experimental selectivities collected from diverse reactions is now a well-established approach for assessing the broad applicability of a catalyst. [6][7][8][9][10] A powerful way for interpreting the results of these experiments is to calculate Figure 1. Theoretical comparison of a specific catalyst that works well on challenging transformations compared to a general catalyst that demonstrates good selectivities throughout.…”

Section: Measuring Broad Spectrum Successmentioning

confidence: 99%

Relative Generality and Risk: Quantitative Measures For Broad Catalyst Success

Sanocki,

Russell,

Handjaya

et al. 2024

Preprint

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The performance of chiral catalysts is typically evaluated against empirical reaction outputs like yield and selectivity with traditional analyses limited to a single model system. Expansion of the reaction space permits catalysts to be as-sessed for generality and this provides another useful metric for measuring the effectiveness of a catalyst. The catalyst generality algorithm will assign quantitative generality values to catalyst structures but such broad assessments are applied with the assumption that the reactions under evaluation are more or less the same by disregarding any inherent challenges associated with a particular reaction class. To address this limitation, we introduce two new met-rics, relative generality and risk. These are designed to correct for variations in reaction difficulty and enable a more nuanced evaluation of catalyst performance relative to the specific demands of each reaction. We show in a number of challenging examples that these metrics allow researchers to distinguish between catalysts genuinely exhibiting superior performance and those appearing favorable due to application toward less demanding reactions. This represents a significant advancement in quantifying catalyst success, with demonstrated applications in retrospective analyses and early insights into emerging catalyst classes.

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“…4 Finding species with enhanced substrate breadth requires evaluating wider regions of chemical space derived from a large matrix of diverse catalysts crossed with a panel of substrates that effectively represent the whole target molecule class. Today, "one-pot-multisubstrate" screening [5][6][7] is tractable with highthroughput experimentation techniques, [8][9][10][11][12] but has found limited applicability due to issues associated with chemical compatibility and product analysis. The catalyst space investigated remains limited, at best, to tens of candidates and, perhaps worse, the most general ones might be unwittingly excluded from the original screening set, biasing the results.…”

Section: Introductionmentioning

confidence: 99%