Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

Reid, Jolene P.; Proctor, Rupert S. J.; Sigman, Matthew S.; Phipps, Robert J.

doi:10.1021/jacs.9b11658

Cited by 87 publications

(51 citation statements)

References 40 publications

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“…In an attempt to extend the scope of their earlier asymmetric aminoalkylation method beyond pyridines and quinolines, Phipps in collaboration with the Sigman group adopted a multivariate linear regression (MLR) analysis as a predictive approach for the exploration of substrate scope across the substrate classes in an efficient manner (Scheme 18). [54] As per the prediction of the model parameters substrate scope of previously developed Ir‐photoredox and chiral phosphoric acid catalyzed asymmetric aminoalkylation method was extended to pharmaceutically relevant pyrimidines and pyrazines. Importantly, in most of the cases final products 29 were obtained with high enantioselectivities and correlated well with the values predicted through MLR analysis.…”

Section: C−h Functionalization Of Heteroarenesmentioning

confidence: 99%

Late‐Stage Alkylation of Heterocycles Using N‐(Acyloxy)phthalimides

Parida

Hota

Kumar

et al. 2021

Chemistry An Asian Journal

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Synthetic methods enabling late-stage modification of heterocycles hold tremendous importance in the pharmaceutical and agrochemical industry and drug discovery. Accordingly, efficient, functional group tolerant and selective late-stage alkylation of valuable molecular entities is of enormous significance and well-acknowledged in medicinal chemistry. Radical alkylation of heteroarenes employing carboxylic acids as the alkyl radical precursor represents one of the most direct ways of CÀ H functionalizations of heterocycles. Recently, the field has undergone a revolutionary development especially with regard to the generation of alkyl radicals under much milder conditions. In this regard N-(acyloxy)phthalimides (NHPI esters) have emerged as a suitable precursor of a diverse set of alkyl radicals allowing formal CÀ H alkylation of not only N-heteroarenes but a diverse set of non-aromatic heterocycles under visible light photocatalysis or electrochemical conditions. This review delineates all these discoveries and provides readers a comprehensive overview of this rapidly expanding field.

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Section: C−h Functionalization Of Heteroarenesmentioning

confidence: 99%

Late‐Stage Alkylation of Heterocycles Using N‐(Acyloxy)phthalimides

Parida

Hota

Kumar

et al. 2021

Chemistry An Asian Journal

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“…Machine learning is a broad concept that includes many methods, such as artificial neural networks [100], support vector machines [101], linear regression [102], and kernel methods [103]. The methods used in catalyst discovery and optimization are not uniform, and sometimes different methods are used simultaneously.…”

Section: Machine Learning In Catalystsmentioning

confidence: 99%

Molecular Dynamics and Machine Learning in Catalysts

Liu

Zhu

et al. 2021

Catalysts

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Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

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“…SPDBs of homogeneous catalysts are currently not available [28]. SPDBs with a dense representation of the chemical space of catalytic scaffolds can help discover design principles leading to development of sustainable catalytic systems for various applications [36,37,28,38,39,40,41]. Representations of the molecular structure is of high importance in SPDBs.…”

Section: Introductionmentioning

confidence: 99%

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

Kalikadien¹,

Pidko²,

Sinha

2021

Preprint

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<div>Local chemical space exploration of an experimentally synthesized material can be done by making slight structural</div><div>variations of the synthesized material. This generation of many molecular structures with reasonable quality,</div><div>that resemble an existing (chemical) purposeful material, is needed for high-throughput screening purposes in</div><div>material design. Large databases of geometry and chemical properties of transition metal complexes are not</div><div>readily available, although these complexes are widely used in homogeneous catalysis. A Python-based workflow,</div><div>ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule, is introduced.</div><div>The overall computational workflow of ChemSpaX is explained in more detail. ChemSpaX uses 3D information,</div><div>to place functional groups on an input structure. For example, the input structure can be a catalyst for which one</div><div>wants to use high-throughput screening to investigate if the catalytic activity can be improved. The newly placed</div><div>substituents are optimized using a computationally cheap force-field optimization method. After placement of</div><div>new substituents, higher level optimizations using xTB or DFT instead of force-field optimization are also possible</div><div>in the current workflow. In representative applications of ChemSpaX, it is shown that the structures generated by</div><div>ChemSpaX have a reasonable quality for usage in high-throughput screening applications. Representative applications</div><div>of ChemSpaX are shown by investigating various adducts on functionalized Mn-based pincer complexes,</div><div>hydrogenation of Ru-based pincer complexes, functionalization of cobalt porphyrin complexes and functionalization</div><div>of a bipyridyl functionalized cobalt-porphyrin trapped in a M2L4 type cage complex. Descriptors such as</div><div>the Gibbs free energy of reaction and HOMO-LUMO gap, that can be used in data-driven design and discovery</div><div>of catalysts, were selected and studied in more detail for the selected use cases. The relatively fast GFN2-xTB</div><div>method was used to calculate these descriptors and a comparison was done against DFT calculated descriptors.</div><div>ChemSpaX is open-source and aims to bolster the efforts of the scientific community towards data-driven material</div><div>discovery.</div>

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Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines

Cited by 87 publications

References 40 publications

Late‐Stage Alkylation of Heterocycles Using N‐(Acyloxy)phthalimides

Late‐Stage Alkylation of Heterocycles Using N‐(Acyloxy)phthalimides

Molecular Dynamics and Machine Learning in Catalysts

ChemSpaX: Exploration of Chemical Space by Automated Functionalization of Molecular Scaffold

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