A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(3), P. 1205 - 1217

Published: Jan. 12, 2022

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number potential requires pruning candidate space by efficient prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library can be used to build predictive models for catalyst performance serve as blueprint novel designs. Herein we introduce kraken, discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based representative conformer ensembles. Using quantum-mechanical methods, calculated 1558 ligands, including commercially available examples, trained machine learning predict properties over 300000 new ligands. We demonstrate application kraken systematically explore organophosphorus how existing data sets catalysis accelerate ligand selection during reaction optimization.

Language: Английский

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Emerging Trends in Cross-Coupling: Twelve-Electron-Based L₁Pd(0) Catalysts, Their Mechanism of Action, and Selected Applications

Chemical Reviews, Journal Year: 2022, Volume and Issue: 122(23), P. 16983 - 17027

Published: Oct. 3, 2022

Monoligated palladium(0) species, L

Language: Английский

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Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Chemical Science, Journal Year: 2022, Volume and Issue: 14(2), P. 226 - 244

Published: Nov. 28, 2022

This review outlines several organic chemistry tasks for which predictive machine learning models have been and can be applied.

Language: Английский

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Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 145(1), P. 110 - 121

Published: Dec. 27, 2022

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe machine learning workflow for multi-objective optimization catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through two sequential required in asymmetric synthesis an active pharmaceutical ingredient. To accomplish this, density functional theory-derived database >550 ligands constructed, designer chemical space mapping technique established. The protocol used classification methods identify catalysts, followed by linear regression model selectivity. led prediction validation significantly improved all outputs, suggesting general strategy can be readily implemented optimizations where performance is controlled

Language: Английский

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On the use of real-world datasets for reaction yield prediction

Chemical Science, Journal Year: 2023, Volume and Issue: 14(19), P. 4997 - 5005

Published: Jan. 1, 2023

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such have been made available. first real-world dataset ELNs pharmaceutical company disclosed its relationship to high-throughput experimentation (HTE) described. For chemical yield predictions, task synthesis, an attributed graph neural network (AGNN) performs as well or better than best previous models on two HTE Suzuki-Miyaura Buchwald-Hartwig reactions. However, training AGNN ELN does not lead predictive model. implications using data ML-based are discussed context predictions.

Language: Английский

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Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(15), P. 8689 - 8699

Published: April 4, 2023

While the oxidative addition of Ni(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding this fundamental process is still lacking. Herein, we describe a detailed study using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement rates for diverse set iodide substrates four classes catalytically relevant complexes (Ni(MeBPy), Ni(MePhen), Ni(Terpy), Ni(BPP)). With >200 experimental rate measurements, were able identify essential electronic steric factors impacting through multivariate linear regression models. This led classification mechanisms, either three-center concerted or halogen-atom abstraction pathway based on ligand type. A global heat map predicted was created shown applicable better reaction outcome case Ni-catalyzed coupling reaction.

Language: Английский

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Generality-oriented optimization of enantioselective aminoxyl radical catalysis

Science, Journal Year: 2023, Volume and Issue: 380(6646), P. 706 - 712

Published: May 18, 2023

Catalytic enantioselective methods that are generally applicable to a broad range of substrates rare. We report strategy for the oxidative desymmetrization

Language: Английский

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¹⁴N to ¹⁵N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(5), P. 2950 - 2958

Published: Jan. 29, 2024

The selective modification of nitrogen heteroaromatics enables the development new chemical tools and accelerates drug discovery. While methods that focus on expanding or contracting skeletal structures are emerging, for direct exchange single core atoms remain limited. Here, we present a method 14N → 15N isotopic several aromatic heterocycles. This isotope transmutation occurs through activation heteroaromatic substrate by triflylation atom, followed ring-opening/ring-closure sequence mediated 15N-aspartate to effect atom. Key success this transformation is formation an isolable 15N-succinyl intermediate, which undergoes elimination give isotopically labeled heterocycle. These transformations occur under mild conditions in high yields.

Language: Английский

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

Science Advances, Journal Year: 2024, Volume and Issue: 10(3)

Published: Jan. 17, 2024

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

Language: Английский

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Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(5), P. 3043 - 3051

Published: Jan. 26, 2024

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)–C(sp3) bonds. These reactions are most often catalyzed by nickel complexes nitrogenous ligands, especially 2,2′-bipyridines. Precise prediction, selection, design optimal ligands remains challenging, despite significant increases in reaction scope mechanistic understanding. Molecular parameterization statistical modeling provide a path to development improved bipyridine that will enhance selectivity existing broaden electrophiles can be coupled. Herein, we describe generation computational ligand library, correlation observed outcomes with features silico Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted display 5-fold increase product formation versus homodimerization when compared current state art. This yield was general several couplings, including challenging aryl chloride N-alkylpyridinium salt.

Language: Английский

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