Synthesis and troubleshooting of chiral 1,2-amino alcohols leading to highly substituted bisoxazoline ligands

Kwong, Adon Calvin

Permalink

https://hdl.handle.net/2142/116139 Copy

Description

Title

Synthesis and troubleshooting of chiral 1,2-amino alcohols leading to highly substituted bisoxazoline ligands

Author(s)

Kwong, Adon Calvin

Issue Date

2022-05-17

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

Denmark, Scott E

Department of Study

Chemistry

Discipline

Chemistry

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Bisoxazoline
• Ligand
• 1,2-Amino Alcohols
• Chemoinformatics

Abstract

In asymmetric reaction development, catalyst design has generally been driven by empirical optimization. Chemoinformatics can provide an attractive alternative by predicting selective catalysts with trained machine learning algorithms. Our group is currently developing algorithms to generate descriptors that quantify steric and electronic properties for thousands of candidate molecules. Synthesizing and evaluating stereoselective ligands is crucial in optimizing the algorithm and producing more refined predictions. Chapter 1 discusses the background of 2,2’-(methylene)bisoxazoline (BOX) ligands in the context of chemoinformatics. Within the chemoinformatic workflow, generation of a large in silico library of synthetically accessible ligands with structural diversity is required. The BOX ligand was identified as a desirable ligand class because its highly modular and diversifiable scaffold. To synthesize representatives to generate data for machine-learning modeling, a general, asymmetric synthesis of novel 4,5-polysubstituted bisoxazoline ligands was devised. Chapter 2 discusses the development of novel unified strategy for the asymmetric synthesis of highly substituted BOX ligands. Exploration of the method to construct chiral 1,2-amino alcohols through addition of aryl lithium reagents to Ellman sulfinimines, followed by construction of the BOX ligands, is discussed. Chapter 3 discusses troubleshooting and optimization of key steps in the synthesis of a brief selection of low-yielding bisoxazoline ligands. Certain BOX ligands required for other chemoinformatics projects were problematic to synthesize due to several low-yielding steps, making it implausible to furnish enough to generate data in machine-learning modeling. Individual optimization is attempted to achieve gram-scale synthesis for target bisoxazolines.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Adon Kwong

Owning Collections