Ring Expansion Strategies for the Synthesis of Medium Sized Ring and Macrocyclic Sulfonamides DOI Creative Commons
Zhongzhen Yang,

Illya Zalessky,

Ryan G. Epton

и другие.

Angewandte Chemie, Год журнала: 2023, Номер 135(13)

Опубликована: Янв. 30, 2023

Abstract Two new ring expansion strategies are reported for the synthesis of medium sized and macrocyclic sulfonamides. Both methods can be performed without using classical protecting groups, with key step initiated by nitro reduction amine conjugate addition respectively. Each method used to make diversely functionalised cyclic sulfonamides in good excellent yields, a range sizes. The size dependency synthetic reactions is agreement outcomes modelled Density Functional Theory calculations.

Язык: Английский

Identifying general reaction conditions by bandit optimization DOI
Jason Y. Wang, Jason M. Stevens, Stavros K. Kariofillis

и другие.

Nature, Год журнала: 2024, Номер 626(8001), С. 1025 - 1033

Опубликована: Фев. 28, 2024

Язык: Английский

Процитировано

19

Dataset Design for Building Models of Chemical Reactivity DOI Creative Commons
Priyanka Raghavan, Brittany C. Haas, Madeline E. Ruos

и другие.

ACS Central Science, Год журнала: 2023, Номер 9(12), С. 2196 - 2204

Опубликована: Дек. 8, 2023

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development new synthetic processes via, for example, evaluating hypothetical reaction conditions or silico substrate tolerance. Perhaps most determining factor is composition training data whether it sufficient to train model that make accurate predictions over full domain interest. Here, we discuss design datasets ways are conducive data-driven modeling, emphasizing idea set diversity generalizability rely on choice molecular representation. We additionally experimental constraints associated with generating common types chemistry how these considerations should influence dataset building.

Язык: Английский

Процитировано

39

A Data-Driven Workflow for Assigning and Predicting Generality in Asymmetric Catalysis DOI
Isaiah O. Betinol, Junshan Lai,

Saumya Thakur

и другие.

Journal of the American Chemical Society, Год журнала: 2023, Номер 145(23), С. 12870 - 12883

Опубликована: Июнь 2, 2023

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

Язык: Английский

Процитировано

33

Branched-Selective Cross-Electrophile Coupling of 2-Alkyl Aziridines and (Hetero)aryl Iodides Using Ti/Ni Catalysis DOI
Wendy L. Williams,

Neyci E. Gutiérrez-Valencia,

Abigail G. Doyle

и другие.

Journal of the American Chemical Society, Год журнала: 2023, Номер 145(44), С. 24175 - 24183

Опубликована: Окт. 27, 2023

The arylation of 2-alkyl aziridines by nucleophilic ring-opening or transition-metal-catalyzed cross-coupling enables facile access to biologically relevant β-phenethylamine derivatives. However, both approaches largely favor C–C bond formation at the less-substituted carbon aziridine, thus enabling only linear products. Consequently, despite attractive disconnection that it poses, synthesis branched arylated products from has remained inaccessible. Herein, we address this long-standing challenge and report first branched-selective with aryl iodides. This unique selectivity is enabled a Ti/Ni dual-catalytic system. We demonstrate robustness method twofold approach: an additive screening campaign probe functional group tolerance feature-driven substrate scope study effect local steric electronic profile each coupling partner on reactivity. Furthermore, diversity generation predictive reactivity models guided mechanistic understanding. Mechanistic studies demonstrated arises TiIII-induced radical aziridine.

Язык: Английский

Процитировано

27

Hexafluoroisopropanol (HFIP) as a Multifunctional Agent in Gold-Catalyzed Cycloisomerizations and Sequential Transformations DOI
Nikolaos V. Tzouras, Leandros P. Zorba, Entzy Kaplanai

и другие.

ACS Catalysis, Год журнала: 2023, Номер 13(13), С. 8845 - 8860

Опубликована: Июнь 20, 2023

Despite the unique position of gold catalysis in contemporary organic synthesis, this area research is notorious for requiring activators and/or additives that enable by generating cationic forms catalysts. Cycloisomerization reactions occupy a significant portion gold-catalyzed reaction space, while they represent diverse family are frequently utilized synthesis. Herein, hexafluoroisopropanol (HFIP) shown to be uniquely simple tool cycloisomerizations, rendering use external obsolete and leading highly active catalytic systems with ppm levels catalyst loading certain cases. HFIP assumes dual role as solvent an activator, operating via dynamic activation Au–Cl bond through hydrogen bonding, which initiates cycle. This special mode can efficient scalable cyclization propargylamides ynoic acids [AuCl(L)] complexes. A thorough screening ancillary ligands counter anions has been performed, establishing methodology alternative elaborate ligand/catalyst design activators. Additionally, concept applied C–C bond-forming cycloisomerization 2H-chromenes sequential or one-pot transformations activated ketoesters, functionalized N-heterocyclic carbene (NHC) precursor salt, compound bearing bioactive indole core, among others. Importantly, mechanistic investigations, including "snapshot" species interest solid state, we were able unambiguously detect key H-bonding interaction between catalyst, shedding light on intermolecular enables catalysis. In cases examined herein, not only excellent but also potent activator valuable synthetic handle when incorporated into functional groups products.

Язык: Английский

Процитировано

25

Negative Data in Data Sets for Machine Learning Training DOI Open Access
Michael P. Maloney,

Connor W. Coley,

Samuel Genheden

и другие.

The Journal of Organic Chemistry, Год журнала: 2023, Номер 88(9), С. 5239 - 5241

Опубликована: Апрель 26, 2023

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTNegative Data in Sets for Machine Learning TrainingMichael P. MaloneyMichael MaloneyDepartment of Chemistry and Biochemistry, University Notre Dame, Indiana 46556, United StatesMore by Michael Maloneyhttps://orcid.org/0009-0001-3385-7567, Connor W. ColeyConnor ColeyDepartment Chemical Engineering Department Electrical Computer Science, Massachusetts Institute Technology, Cambridge, 02139, Coleyhttps://orcid.org/0000-0002-8271-8723, Samuel GenhedenSamuel GenhedenMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore Genhedenhttps://orcid.org/0000-0002-7624-7363, Nessa CarsonNessa CarsonEarly Development, Pharmaceutical Macclesfield SK10 2NA, U.K.More Carsonhttps://orcid.org/0000-0002-2769-1775, Paul HelquistPaul HelquistDepartment Helquisthttps://orcid.org/0000-0003-4380-9566, Per-Ola NorrbyPer-Ola NorrbyData Science Modelling, Norrbyhttps://orcid.org/0000-0002-2419-0705, Olaf Wiest*Olaf WiestDepartment States*Email: [email protected]More Wiesthttps://orcid.org/0000-0001-9316-7720Cite this: J. Org. Chem. 2023, 88, 9, 5239–5241Publication Date (Web):April 26, 2023Publication History Received17 April 2023Published online26 inissue 5 May 2023https://pubs.acs.org/doi/10.1021/acs.joc.3c00844https://doi.org/10.1021/acs.joc.3c00844editorialACS PublicationsCopyright © Published 2023 American Society. This publication is available under these Terms Use. Request reuse permissions free to access through this site. Learn MoreArticle Views4617Altmetric-Citations6LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InRedditEmail (853 KB) Get e-AlertscloseSUBJECTS:Addition reactions,Chemical reactions,Machine learning,Materials,Organic synthesis e-Alerts

Язык: Английский

Процитировано

24

Negative Data in Data Sets for Machine Learning Training DOI Open Access
Michael P. Maloney,

Connor W. Coley,

Samuel Genheden

и другие.

Organic Letters, Год журнала: 2023, Номер 25(17), С. 2945 - 2947

Опубликована: Апрель 26, 2023

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTNegative Data in Sets for Machine Learning TrainingMichael P. MaloneyMichael MaloneyDepartment of Chemistry and Biochemistry, University Notre Dame, Indiana 46556, United StatesMore by Michael Maloneyhttps://orcid.org/0009-0001-3385-7567, Connor W. ColeyConnor ColeyDepartment Chemical Engineering Department Electrical Computer Science, Massachusetts Institute Technology, Cambridge, 02139, Coleyhttps://orcid.org/0000-0002-8271-8723, Samuel GenhedenSamuel GenhedenMolecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, SwedenMore Genhedenhttps://orcid.org/0000-0002-7624-7363, Nessa CarsonNessa CarsonEarly Development, Pharmaceutical Macclesfield SK10 2NA, U.K.More Carsonhttps://orcid.org/0000-0002-2769-1775, Paul HelquistPaul HelquistDepartment Helquisthttps://orcid.org/0000-0003-4380-9566, Per-Ola NorrbyPer-Ola NorrbyData Science Modelling, Norrbyhttps://orcid.org/0000-0002-2419-0705, Olaf Wiest*Olaf WiestDepartment States*Email: [email protected]More Wiesthttps://orcid.org/0000-0001-9316-7720Cite this: Org. Lett. 2023, 25, 17, 2945–2947Publication Date (Web):April 26, 2023Publication History Received19 April 2023Published online26 inissue 5 May 2023https://pubs.acs.org/doi/10.1021/acs.orglett.3c01282https://doi.org/10.1021/acs.orglett.3c01282editorialACS PublicationsCopyright © Published 2023 American Society. This publication is available under these Terms Use. Request reuse permissions free to access through this site. Learn MoreArticle Views7805Altmetric-Citations5LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum full text article downloads since November 2008 (both PDF HTML) across all institutions individuals. These metrics regularly updated reflect usage leading up last few days.Citations number other articles citing article, calculated Crossref daily. Find more information about citation counts.The Altmetric Attention Score a quantitative measure attention that research has received online. Clicking on donut icon will load page at altmetric.com with additional details score social media presence given article. how calculated. Share Add toView InAdd Full Text ReferenceAdd Description ExportRISCitationCitation abstractCitation referencesMore Options onFacebookTwitterWechatLinked InRedditEmail (945 KB) Get e-AlertscloseSUBJECTS:Addition reactions,Chemical reactions,Machine learning,Materials,Organic synthesis e-Alerts

Язык: Английский

Процитировано

24

A Modular Strategy for the Synthesis of Macrocycles and Medium-Sized Rings via Cyclization/Ring Expansion Cascade Reactions DOI Creative Commons

Illya Zalessky,

Jack M. Wootton, Jerry K. F. Tam

и другие.

Journal of the American Chemical Society, Год журнала: 2024, Номер 146(8), С. 5702 - 5711

Опубликована: Фев. 19, 2024

Macrocycles and medium-sized rings are important in many scientific fields technologies but hard to make using current methods, especially on a large scale. Outlined herein is strategy by which functionalized macrocycles can be prepared cyclization/ring expansion (CRE) cascade reactions, without resorting high dilution conditions. CRE reactions designed operate exclusively via kinetically favorable 5–7-membered ring cyclization steps; this means that the problems typically associated with classical end-to-end macrocyclization avoided. A modular synthetic approach has been developed facilitate simple assembly of requisite linear precursors, then converted into an extremely broad range one nine protocols.

Язык: Английский

Процитировано

10

Bridging the information gap in organic chemical reactions DOI
Malte L. Schrader, Felix Schäfer,

Felix Schäfers

и другие.

Nature Chemistry, Год журнала: 2024, Номер 16(4), С. 491 - 498

Опубликована: Март 28, 2024

Язык: Английский

Процитировано

9

Small Data Can Play a Big Role in Chemical Discovery DOI Creative Commons
Hadas Shalit Peleg, Anat Milo

Angewandte Chemie International Edition, Год журнала: 2023, Номер 62(26)

Опубликована: Март 23, 2023

The chemistry community is currently witnessing a surge of scientific discoveries in organic supported by machine learning (ML) techniques. Whereas many these techniques were developed for big data applications, the nature experimental often confines practitioners to small datasets. Herein, we touch upon limitations associated with ML and emphasize impact bias variance on constructing reliable predictive models. We aim raise awareness possible pitfalls, thus, provide an introductory guideline good practice. Ultimately, stress great value statistical analysis data, which can be further boosted adopting holistic data-centric approach chemistry.

Язык: Английский

Процитировано

18