Synthesis of universal training sets and applications of chemoinformatics toward enantioselective reaction optimization

Rose, Brennan T.

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https://hdl.handle.net/2142/115918 Copy

Description

Title

Synthesis of universal training sets and applications of chemoinformatics toward enantioselective reaction optimization

Author(s)

Rose, Brennan T.

Issue Date

2022-07-15

Director of Research (if dissertation) or Advisor (if thesis)

Denmark, Scott E

Doctoral Committee Chair(s)

Denmark, Scott E

Committee Member(s)

• Burke, Martin D
• Sarlah, David
• Fout, Alison R

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Chemoinformatics, Chiral Brønsted Acid Catalysis, Iodination, 1,2-Amino alcohols

Abstract

This thesis covers three independent projects with highlights of my experimental contributions to all three projects and a detailed discussion of my application of the chemoinformatics workflow. A brief introduction to 3D-QSSR comprises the first half of chapter 1, followed by the development of a general method for the synthesis of 1,2-amino alcohols. Chapter 2 centers on the development of a chemoinformatics workflow, its application to chiral phosphoric acids, the synthesis of chiral phosphoric acids, and the validation of this proposed workflow. Prediction of highly selective catalysts (>80% ee) with high levels of accuracy are discussed towards the end of chapter 2. Chapter 3 of this thesis will cover the generation of an in silico library and selection of a universal training set (UTS) of disulfonimide (DSI) catalysts. The nearest neighbors analysis that was performed to ensure data integrity for modeling purposes is discussed throughout chapter 3. Finally, some excerpts of the synthesis of this UTS are provided. Chapter 4 will focus on the application of this DSI UTS towards the atropselective iodination of 2-amino-6-aryl pyridines. The development of a large diverse data set and a discussion of different approaches to regression modeling of this data set are presented. Finally, development of a new catalyst recommendation system called catalyst selection by committee (CSC) that is based heavily on high-level data fusion strategies is discussed towards the end of chapter 4. Initial validation of this CSC workflow by the synthesis of three of the proposed catalysts lead to the discovery of a significantly more general catalyst, whose application towards the preparative scale iodination of 2-amino-6-aryl-pyridines will be discussed.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2020 Brennan T. Rose

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