Computational and Experimental Studies of Selective Rhodium-Catalyzed Allylic C–H Functionalization through π-Allyl Intermediates and Computational Development of Macrocycle Leads for Undruggable Targets Open Access

Abstract

Allylic C–H functionalization methodology has been developed as an atom-economical way of accessing π-allyl intermediates to form allylic products. Recent advances in the Blakey group have resulted in the development of a regioselective amination methodology tolerating the use of amines with a single electron-withdrawing group as the nucleophile, implying greater synthetic utility. An efficient synthesis of Sensipar using this amination procedure with an allylbenzene derivative was proposed. Optimization studies on a model system were then pursued to provide a yield double the previously disclosed results. Unfortunately, applying these conditions with the target amine required for this synthetic plan, resulted in oxidation of the substrate, indicating incompatibility. Following a study of functional group tolerance of the allylic coupling partners, where low yields were observed, an iridium-catalyzed branched-selective method was developed. Further catalyst design within the Blakey group resulted in the development of an asymmetric rhodium indenyl catalyst capable of performing an allylic C–H amidation with high enantio- and regioselectivity. Computational studies were undertaken to investigate the source of these selectivities. Through the development of a full energy profile, it was determined that the two transition states of the formation of the π-allyl complex distinguished selectivity through the alignment of the allylbenzene to the catalyst; the energetic difference matched the experimentally observed enantiomeric ratio. The regioselectivity was determined by the steric interaction of the amide source to the π-allyl intermediate in the C–N bond forming step. Determining potential directions for future C–H functionalization methodology led to a computational investigation of leads for ”undruggable” protein targets, those with binding surfaces rather than a well-defined binding pocket. The semi-rigid nature of macrocycles has been implicated as a way of attaining affinity for these binding surfaces. Due to challenges in attaining structural data of target interactions from which to design drugs, high throughput screening of biologically synthesized macrocyclic peptides has been utilized to identify hits. Problems arise with the bioavailability of these structures due to their large number of amide linkages, indicating a need for more structurally diverse macrocycles. Using a macrocyclic peptide which displayed high affinity to a given target as a template of functionality for these non-peptide macrocycles can be accomplished through machine learning techniques. A starting set for this computation has been developed using randomly connected elements of a fragment library, with particular attention to ensuring their druglike nature. Machine learning computations on this set should then result in targets for these undruggable proteins and aims for future synthesis.

Table of Contents

Contents

1 Allylic Amination 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Mechanism of Allylic Amidation 8

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Development of Structurally Diverse Macrocycles for Undruggable Targets 12

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Appendix A

Supplemental Information 22

A.1 General Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

A.2 Allylic Amination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

A.3 Computational Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

A.4 Macrocycle Starting Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Bibliography 31

List of Figures 1.1

General Reaction Scheme for Pre-Oxidized Alkenes . . . . . . . . . . . . . . 2

1.2 Palladium-Catalyzed C–H Oxygenation . . . . . . . . . . . . . . . . . . . . . 2

1.3 Rhodium-Catalyzed Intramolecular C–H Amination . . . . . . . . . . . . . . 2

1.4 Rhodium-Catalyzed Intermolecular C–H Amination . . . . . . . . . . . . . . 3

1.5 Retrosynthetic Analysis of Sensipar . . . . . . . . . . . . . . . . . . . . . . . 3

1.6 Elimination of Ethylnapthylamine to form Vinylnaphthalene . . . . . . . . . 5

1.7 Substrate Scope of Substituted Allylbenzene Structures . . . . . . . . . . . . 6

2.1 Ring Slip on Indenyl Ligand . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Successful Enantioselective Amidation of Allylbenzene with 2-methyl 3-phenyl Indene Catalyst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Energy Profile of Enantioselective Amidation . . . . . . . . . . . . . . . . . . 11

3.1 Project Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2 Randomly Selected Fragments . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3 Fragment Library Distribution Along Predictive Criteria for Druglike Nature 16

3.4 Randomly Selected Examples of 3, 4, and 5 Fragment Molecules . . . . . . . 18

3.5 Connected Molecule Distributions Along Predictive Criteria for Druglike Nature 19

3.6 Possible Connections for a Three Fragment Macrocycle . . . . . . . . . . . . 19

3.7 Example Macrocycles from 3 and 4 Fragment Molecules . . . . . . . . . . . . 20

A.1 Calibration Curve of Nonane . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

A.2 Calibration Curve of Allylbenzene . . . . . . . . . . . . . . . . . . . . . . . . 24

A.3 Calibration Curve of Aminated Allylbenzene . . . . . . . . . . . . . . . . . . 25

List of Tables

1.1 Optimization Table of Amination Conditions on Allylbenzene . . . . . . . . 4

About this Honors Thesis

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School	Emory College
Department	Chemistry
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Chemistry, Organic
Keyword	Amidation Amination Allylic C-H functionalization Machine Learning Macrocycles π-Allyl C-H functionalization
Committee Chair / Thesis Advisor	Simon Blakey, Emory University
Committee Members	David Zureick-Brown, Emory University Cora MacBeth, Emory University

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