Asymmetric Catalytic Ketone Hydrogenation: Relating Substrate Structure and Product Enantiomeric Excess Using QSPR

Linden, J. B. Van Der; Ras, Erik‐Jan; Hooijschuur, Simone M.; Klaus, Guido; Luchters, Niels T.J.; Dani, Paulo; Verspui, Göran; Smith, Alan A.; Damen, Eric W. P.; McKay, Ben; Hoogenraad, Marcel

doi:10.1002/qsar.200420060

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…Several modeling/experiment cycles may be necessary before finding a compound with the specific combination of properties. While extensively employed in pharmaceutical 17,19,20 and agrochemical 16,18,21 research, virtual screening approaches have only started to be applied to the development of catalysts [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] and materials [42][43][44][45][46][47] recently.…”

Section: Introductionmentioning

confidence: 99%

Quantitative structure–reactivity modeling of copper-catalyzed atom transfer radical polymerization

Lena

Chai

2010

Polym. Chem.

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In this work, we present the first successful application of in silico modeling to the construction of quantitative and predictive relationships between the set of constants k act , k deact and K ATRP and the structures and properties of various ATRP catalysts and initiators. The results are consistent not only with the generally accepted ATRP mechanistic picture but also provide valuable insights into this complex polymerization reaction. The models, built using the genetic function approximation algorithm, highlight and quantify the pivotal roles played in the ATRP process by energetic and steric factors of both catalysts and initiators as well as by the reaction medium. Moreover, the models suggest the existence of long-range interactions in catalyst-initiator recognition and subsequent binding. We believe that the approach will prove to be a powerful tool for the discovery of improved catalysts for ATRP.Scheme 1 Atom transfer radical polymerization equilibrium.

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Section: Introductionmentioning

confidence: 99%

Quantitative structure–reactivity modeling of copper-catalyzed atom transfer radical polymerization

Lena

Chai

2010

Polym. Chem.

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“…A similar study performed by these researchers has examined the catalytic, enantioselective hydrogenation of ketones with chiral ruthenium complexes (Figure 68). 218 Thirteen unsymmetrical benzophenone derivatives are subjected to Noyori enantioselective hydrogenation and the results are used to construct a PLS model correlating structure to reaction outcome. The descriptors employed are obtained using DRAGON software 219 with 3D-structures as input, then removing descriptors that are highly skewed.…”

Section: Other Applications Of Qssr In Enantioselective Catalysismentioning

confidence: 99%

Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

2019

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The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure−selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.1644 3.3. Perspective on CCM 1649 4. Chirality Codes 1649 4.1. Introduction to Chirality Codes 1649 4.2. Application of CICC 1649 4.3. Other Chirality Codes 1654 4.4. Conclusion and Perspective 1654 5. Molecular Interaction Field (MIF) Based Methods 1655 5.1. Alignment Dependent MIF Methods 1655 5.1.1. Background to Alignment Dependent MIF Methods 1655 5.1.2. Applications of Alignment Dependent MIF-Based Methods in Asymmetric Catalysis 1656 5.1.3.

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“…Other workers have embraced this approach with success ix,x,xi,xii, xiii. In addition, intriguing reports utilizing QSSR models to predict substrate enantioselection with chiral catalysts have appeared xiv,xv. Here, we report our independent efforts toward generating models to predict enantioselection for substrates which has also led to a tool to identify potential mechanistic differences between substrate classes.…”

Section: Introductionmentioning

confidence: 98%

Quantum molecular interaction field models of substrate enantioselection in asymmetric processes☆

Kozlowski¹,

Ianni²

2010

Journal of Molecular Catalysis A: Chemical

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Computational models correlating substrate structure to enantioselection with asymmetric catalysts using the QMQSAR program are described. In addition to rapidly providing predictions that could be used to facilitate the screening of catalysts for novel substrates, the QMQSAR program identifies the portions of the substrate that most directly influence the enantioselectivity. The lack underlying relationship between all the substrates in one case, requires two quantitative structure selectivity relationships (QSSR) models to describe all of the experimental results.

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Asymmetric Catalytic Ketone Hydrogenation: Relating Substrate Structure and Product Enantiomeric Excess Using QSPR

Cited by 16 publications

References 23 publications

Quantitative structure–reactivity modeling of copper-catalyzed atom transfer radical polymerization

Quantitative structure–reactivity modeling of copper-catalyzed atom transfer radical polymerization

Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Quantum molecular interaction field models of substrate enantioselection in asymmetric processes☆

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