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Leveraging Computational Chemistry a...

Haas, Brittany Catherine,

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Leveraging Computational Chemistry and Data Science Tools to Enhance Reaction Development /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Leveraging Computational Chemistry and Data Science Tools to Enhance Reaction Development // Brittany Catherine Haas.
作者:	Haas, Brittany Catherine,
面頁冊數:	1 electronic resource (282 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
標題:	Chemistry. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31243271
ISBN:	9798342715478

Leveraging Computational Chemistry and Data Science Tools to Enhance Reaction Development /
Haas, Brittany Catherine,

Leveraging Computational Chemistry and Data Science Tools to Enhance Reaction Development /Brittany Catherine Haas. - 1 electronic resource (282 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Data science has emerged as a valuable approach to probe structure-activity relationships and for reaction development in synthetic organic chemistry. This dissertation describes how computational chemistry and data science techniques can be integrated to design datasets and develop interpretable statistical models for reactivity that inform reaction development campaigns. Chapter 1 provides an in-depth summary of the methods employed in this dissertation and the necessary considerations when using them.Chapters 2 and 3 relate to carboxylic acids and alkyl amines in the context of the ubiquitous amide coupling reaction. The initial study outlines intentional substrate selection along with the collection of reaction rate data and interpretable molecular descriptors that allowed for the development of a statistical model for amide coupling rates and enabled the prediction of rates for untested carboxylic acid/amine pairs. In order to eliminate the cost of descriptor calculation for data-driven reaction development and predictive models of reactivity, we established models that enable high-fidelity predictions for a vast number of potential carboxylic acid and alkyl amine substrates. Graph neural networks trained on expansive DFT-level descriptor libraries of these common reactants were used to rapidly predict conformationally-dependent DFT-level descriptors and demonstrated to be suitable for downstream applications using the data from Chapter 2.Chapters 4 and 5 each present a method for the enantioselective synthesis of sulfonimidamides, an underexplored sulfur (VI) functional group. First, a palladium-catalyzed cross-coupling for the enantioselective aryl-carbonylation of sulfonimidamides is presented. Bisphosphine ligand, aryl and heteroaryl iodide, and sulfonimidamide descriptor libraries were used to optimize the reaction and evaluate the scope. Secondly, we reported a desymmetrization of unprotected sulfonimidamides via asymmetric acylation with cinchona alkaloid-phosphinate organocatalysts. A similar data science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling that informed mechanistic studies, serving as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques. Together, these studies offer reliable methods for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores.

English

ISBN: 9798342715478Subjects--Topical Terms:

516420
Chemistry.
Subjects--Index Terms:

Amides

Leveraging Computational Chemistry and Data Science Tools to Enhance Reaction Development /
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520 $a Data science has emerged as a valuable approach to probe structure-activity relationships and for reaction development in synthetic organic chemistry. This dissertation describes how computational chemistry and data science techniques can be integrated to design datasets and develop interpretable statistical models for reactivity that inform reaction development campaigns. Chapter 1 provides an in-depth summary of the methods employed in this dissertation and the necessary considerations when using them.Chapters 2 and 3 relate to carboxylic acids and alkyl amines in the context of the ubiquitous amide coupling reaction. The initial study outlines intentional substrate selection along with the collection of reaction rate data and interpretable molecular descriptors that allowed for the development of a statistical model for amide coupling rates and enabled the prediction of rates for untested carboxylic acid/amine pairs. In order to eliminate the cost of descriptor calculation for data-driven reaction development and predictive models of reactivity, we established models that enable high-fidelity predictions for a vast number of potential carboxylic acid and alkyl amine substrates. Graph neural networks trained on expansive DFT-level descriptor libraries of these common reactants were used to rapidly predict conformationally-dependent DFT-level descriptors and demonstrated to be suitable for downstream applications using the data from Chapter 2.Chapters 4 and 5 each present a method for the enantioselective synthesis of sulfonimidamides, an underexplored sulfur (VI) functional group. First, a palladium-catalyzed cross-coupling for the enantioselective aryl-carbonylation of sulfonimidamides is presented. Bisphosphine ligand, aryl and heteroaryl iodide, and sulfonimidamide descriptor libraries were used to optimize the reaction and evaluate the scope. Secondly, we reported a desymmetrization of unprotected sulfonimidamides via asymmetric acylation with cinchona alkaloid-phosphinate organocatalysts. A similar data science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling that informed mechanistic studies, serving as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques. Together, these studies offer reliable methods for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores.
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