Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Chuang, Kangway V.; Keiser, Michael J.

doi:10.1126/science.aat8603

Cited by 133 publications

(151 citation statements)

References 11 publications

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“…While only a small number of ligands have been considered, the screening of their interactions with other reaction variables is powerful in this case, and this presents significant challenges to many of the familiar, ligand-focussed descriptors reviewed here. We note that this study prompted some controversy and debate while the present contribution was already in review, [107][108][109][110] highlighting that ML in homogeneous catalysis is still in its infancy.…”

Section: Single Class Of Ligandmentioning

confidence: 99%

Computational Ligand Descriptors for Catalyst Design

2019

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Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimisation and design of catalysts.In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,Pdonor ligands and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

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Section: Single Class Of Ligandmentioning

confidence: 99%

Computational Ligand Descriptors for Catalyst Design

2019

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“… 3 – 4 This overarching issue in reaction optimization is often exasperated by subtle connections across several reaction variables, wherein modest structural changes to any or a few of these can have a profound effect on the experimental outcome. 5 – 6 , 7 These factors combined with the number of dimensions under study in most reactions, are the underlying reasons for why optimization is decidedly empirical. 8 – 9 This situation is particularly common in the area of asymmetric catalysis, wherein seemingly minor structural variations to any reaction component can have acute and non-intuitive influences on the observed enantioselectivity.…”

mentioning

confidence: 99%

Holistic prediction of enantioselectivity in asymmetric catalysis

Reid

Sigman

2019

Nature

251

269

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Summary: When faced with unfamiliar reaction space, synthetic chemists typically apply reported conditions (reagents, catalyst, solvent, additives) from closely-related reactions to new substrate types. Unfortunately, this approach often fails due to subtle, albeit important, differences in reaction requirements. Consequently, a significant goal in synthetic chemistry is the ability to transfer chemical observations from one reaction to another, quantitatively. Here, we present such a platform by developing a holistic, data-driven workflow for deriving statistical models for one set of reactions that can be applied to predict out-of-sample examples. As a validating case study, published enantioselectivity data sets that employ BINOL-derived chiral phosphoric acids for a range of nucleophilic addition reactions to imines were combined and statistical models developed. These models reveal the general interactions imparting asymmetric induction and allow the quantitative transfer of this information to new reaction components. The disclosed techniques create opportunities for translating comprehensive reaction analysis to diverse chemical space, streamlining both catalyst and reaction development.

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“…For example, the calculated gauge-independent 13 Step 6. Using the foregoing ISPCA model 1, "2 nd generation" catalysts [15][16][17][18][19][20][21][22] were predicted and synthesized ( Figure 5), focusing on changing characteristics of the catalyst in accordance with the relative weights of the descriptors in the model. Prospective test catalysts were first evaluated using the "molecular ruler" approach wherein, based on ISPCA model 1, a good catalyst must be 1.7 units in PCA space (in any direction) from the nearest training set neighbor in order to expect a substantial change in selectivity (D selectivity = 0.46 or 1 s).…”

Section: Resultsmentioning

confidence: 99%

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

See¹,

Reiner²,

Wen³

et al. 2020

Preprint

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<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

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Comment on “Predicting reaction performance in C–N cross-coupling using machine learning”

Cited by 133 publications

References 11 publications

Computational Ligand Descriptors for Catalyst Design

Computational Ligand Descriptors for Catalyst Design

Holistic prediction of enantioselectivity in asymmetric catalysis

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

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