Ligand Descriptor Analysis in Nickel‐Catalysed Hydrocyanation: A Combined Experimental and Theoretical Study

Burello, Enrico; Marion, P; Galland, Jean-Christophe; Chamard, Alex; Rothenberg, Gadi

doi:10.1002/adsc.200404363

Cited by 39 publications

(23 citation statements)

References 42 publications

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“…[10,11] Note that space B contains molecular descriptor values, rather than structures, but these are related directly to the structures, as we showed recently for the cases of monodentate complexes in Pd-catalysed Heck reactions, [12,13] and bidentate complexes in Ni-catalysed hydrocyanation. [14] Therefore, if one can generate a sufficiently large number of sufficiently diverse structures in space A, and link the spaces A and B, one should be able to predict the relevant figures of merit in space C using QSAR/ QSPR descriptor models. In the following subsection, we will present a building block assembly approach for generating space A, and show that it is relatively easy to obtain a large number of structures (the question of structure diversity is much more complex, and a full treatment of it is out of the scope of this paper [15] ).…”

Section: Theory Defining the Catalyst Spacementioning

confidence: 99%

“…In other cases, the relationship exists but it is not clear, and descriptor selection is indeed the primary task. [14,22] Application to Rh-Catalysed Hydroformylation…”

Section: Predicting Bite Angles Of Bidentate Ligand-rh Complexesmentioning

confidence: 99%

“…[14] The influence is calculated as the variable importance parameter (VIP), calculated using Eq. (2) for a parameter k:…”

Section: Regression Analysismentioning

confidence: 99%

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Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

et al. 2006

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An alternative top-down concept for searching for homogeneous catalysts is introduced. In this approach, three multi-dimensional spaces are considered. These represent the catalysts, the descriptor values (e.g., cone angle, lipophilicity indices), and the figures of merit (e.g., turnover frequency, enantiomeric excess, or product selectivity), respectively. By generating and connecting these spaces, it is possible to screen virtual catalyst libraries and indicate regions in the catalyst space where good catalysts are likely to be found. The generation of the catalyst space from simple building blocks is presented and the application of this approach is demonstrated for two cases: predicting the bite angle in a library of 600 ligandRh complexes, and predicting the linear : branched aldehyde product ratio that these 600 catalysts would give in the hydroformylation of 1-octene. In the latter case, the model is first trained on a set of 39 octene hydroformylation reactions. The limitations and applications of this concept are discussed.

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Section: Theory Defining the Catalyst Spacementioning

confidence: 99%

“…In other cases, the relationship exists but it is not clear, and descriptor selection is indeed the primary task. [14,22] Application to Rh-Catalysed Hydroformylation…”

Section: Predicting Bite Angles Of Bidentate Ligand-rh Complexesmentioning

confidence: 99%

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Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

et al. 2006

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“…We followed a similar approach for the Ni-catalyzed hydrocyanation reaction where PLS analysis is used to develop a QSAR model that relates steric and electronic parameters calculated on a set of 42 ligands with their catalytic performance (TON). [83] The influence of each descriptor on the figure of merit (adiponitrile product yield) is calculated as the VIP parameter and can be seen as the sum over all model dimensions of the variable influence contributions (Figure 8). The charge at the ligating atoms, the rigidity of the molecules, the steric crowding around the metal centre and the bite angle are the most important descriptors evidenced by the PLS model.…”

Section: Partial Least-squares Analysismentioning

confidence: 99%

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Burello

Rothenberg

2006

IJMS

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This review summarises the state-of-the-art methodologies used for designing homogeneous catalysts and optimising reaction conditions (e.g. choosing the right solvent). We focus on computational techniques that can complement the current advances in highthroughput experimentation, covering the literature in the period 1996-2006. The review assesses the use of molecular modelling tools, from descriptor models based on semiempirical and molecular mechanics calculations, to 2D topological descriptors and graph theory methods. Different techniques are compared based on their computational and time cost, output level, problem relevance and viability. We also review the application of various data mining tools, including artificial neural networks, linear regression, and classification trees. The future of homogeneous catalysis discovery and optimisation is discussed in the light of these developments.

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“…[19,20] Some notable examples include Noyoris BINAP ligands, [21] the Josiphos ligands used in the Ciba-Geigy metolachlor process, [22] Jacobsens catalyst, [23,24] and the Keim complexes used in the Shell higher olefins process (SHOP). [25] Although there are several important studies on bidentate ligand descriptors, [26,27] there is only scant information regarding the problem of ligand diversity for library assembly and catalyst selection. [15] Here, we examine measures of diversity for bidentate ligands, with a specific emphasis on the ligand backbone.…”

Section: Introductionmentioning

confidence: 99%

Backbone Diversity Analysis in Catalyst Design

Maldonado¹,

Hageman²,

Migliore³

et al. 2009

Adv Synth Catal

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We present a computer-based heuristic framework for designing libraries of homogeneous catalysts. In this approach, a set of given bidentate ligand-metal complexes is disassembled into key substructures ("building blocks"). These include metal atoms, ligating groups, backbone groups, and residue groups. The computer then rearranges these building blocks into a new library of virtual catalysts. We then tackle the practical problem of choosing a diverse subset of catalysts from this library for actual synthesis and testing. This is not trivial, since catalyst diversity itself is a vague concept. Thus, we first define and quantify this diversity as the difference between key structural parameters (descriptors) of the catalysts, for the specific reaction at hand. Subsequently, we propose a method for choosing diverse sets of catalysts based on catalyst backbone selection, using weighted D-optimal design. The computer selects catalysts with different backbones, where the difference is measured as a distance in the descriptors space. We show that choosing such a D-optimal subset of backbones gives more diversity than a simple random sampling. The results are demonstrated experimentally in the nickel-catalysed hydrocyanation of 3-pentenenitrile to adiponitrile. Finally, the connection between backbone diversity and catalyst diversity, and the implications towards in silico catalysis design are discussed.

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Ligand Descriptor Analysis in Nickel‐Catalysed Hydrocyanation: A Combined Experimental and Theoretical Study

Cited by 39 publications

References 42 publications

Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Backbone Diversity Analysis in Catalyst Design

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