Impact of Design of Experiments in the Optimisation of Catalytic Reactions in Academia

Nori, Valeria; Sinibaldi, Arianna; Pesciaioli, Fabio; Carlone, Armando

doi:10.1055/a-1736-6703

Cited by 5 publications

(3 citation statements)

References 59 publications

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“…In order to optimize a reaction involving many variables, many experiments were needed using the one variable at a time (OVAT) approach. With this in mind, we set out to screen the multidimensional space using Design of Experiments, a tool that is routinely used in industry to optimize catalytic reactions but rather limited in academia [34]. Indeed, via a rational exploration of the chemical space, an industrially appealing protocol for the Michael addition of acetaldehyde to nitroalkenes in water was developed using DoE (Scheme 9c) [33].…”

Section: Scheme 3 Organocatalytic Desymmetrization Of Glutaric Anhydr...mentioning

confidence: 99%

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Asymmetric Organocatalysis—A Powerful Technology Platform for Academia and Industry: Pregabalin as a Case Study

et al. 2022

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Enantioselective organocatalysis has quickly established itself as the third pillar of asymmetric catalysis. It is a powerful technology platform, and it has a tremendous impact in both academic and industrial settings. By focusing on pregabalin, as a case study, this Perspective aims to show how a process amenable to industry of a simple chiral molecule can be tackled in several different ways using organocatalysis.

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Section: Scheme 3 Organocatalytic Desymmetrization Of Glutaric Anhydr...mentioning

confidence: 99%

“…Fortunately, the influence of academia on industry can also be the reverse. For example, academia has adopted the Design of Experiments (DoE), a tool used in industry to optimize reactions or verify the robustness of prospective commercial processes and used it in the realm of organocatalysis as well [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Organocatalysis—A Powerful Technology Platform for Academia and Industry: Pregabalin as a Case Study

et al. 2022

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“…While design of experiment (DoE) and other types of optimizations can indeed help determine the optimal experimental conditions, − these techniques do not address the inherent complexity of finding the optimal catalyst structure. Many ML approaches have emerged to tackle catalysis design but often require large experimental or computed data sets, − a requirement that is often not met in prototypical experimental laboratories.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction

Pereira,

Ruth,

Gerbig

et al. 2024

J. Am. Chem. Soc.

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We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey−Bakshi−Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔG ‡ of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee). This translates into a >50% improvement in relative ΔG ‡ from 0.9 to 1.4 kcal mol −1 . We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a keyintermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model. Our results highlight the synergy of synthetic chemistry and data-centric approaches and provide a blueprint for future catalyst optimization.

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Two photons are better than one: continuous flow synthesis of ꞵ-lactones through a doubly photochemically-activated Paternò-Büchi reaction

Minuto,

Farinini,

De Negri

et al. 2023

J Flow Chem

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In this paper we report a [2 + 2] cycloaddition reaction between ketenes and benzils, characterized by an unusual double photochemical activation triggered by visible light. Employment of a flow system and optimization of reaction conditions through Design of Experiments resulted in moderate to good yields of the corresponding β-lactones. A thorough computational analysis allowed to elucidate the mechanism of the reaction and justify the observed diastereoselectivity. The reaction was also successfully tested with mixed benzils, showing complete regioselectivity. Graphical abstract

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Impact of Design of Experiments in the Optimisation of Catalytic Reactions in Academia

Cited by 5 publications

References 59 publications

Asymmetric Organocatalysis—A Powerful Technology Platform for Academia and Industry: Pregabalin as a Case Study

Asymmetric Organocatalysis—A Powerful Technology Platform for Academia and Industry: Pregabalin as a Case Study

Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey–Bakshi–Shibata Reduction

Two photons are better than one: continuous flow synthesis of ꞵ-lactones through a doubly photochemically-activated Paternò-Büchi reaction

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