Enabling Tools and Techniques for Organic Synthesis E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 804

Veröffentlichungsjahr: 2023

Beschreibung

ENABLING TOOLS AND TECHNIQUES FOR ORGANIC SYNTHESIS Provides the practical knowledge of how new technologies impact organic synthesis, enabling the reader to understand literature, evaluate different techniques, and solve synthetic challenges In recent years, new technologies have impacted organic chemistry to the point that they are no longer the sole domain of dedicated specialists. Computational chemistry, for example, can now be used by organic chemists to help predict outcomes, understand selectivity, and decipher mechanisms. To be prepared to solve various synthetic problems, it is increasingly important for chemists to familiarize themselves with a range of current and emerging tools and techniques. Enabling Tools and Techniques for Organic Synthesis: A Practical Guide to Experimentation, Automation, and Computation provides a broad overview of contemporary research and new technologies applied to organic synthesis. Detailed chapters, written by a team of experts from academia and industry, describe different state-of-the-art techniques such as computer-assisted retrosynthesis, spectroscopy prediction with computational chemistry, high throughput experimentation for reaction screening, and optimization using Design of Experiments (DoE). Emphasizing real-world practicality, the book includes chapters on programming for synthetic chemists, machine learning (ML) in chemical synthesis, concepts and applications of computational chemistry, and more. * Highlights the most recent methods in organic synthesis and describes how to employ these techniques in a reader's own research * Familiarizes readers with the application of computational chemistry and automation technology in organic synthesis * Introduces synthetic chemists to electrochemistry, photochemistry, and flow chemistry * Helps readers comprehend the literature, assess the strengths and limitations of each technique, and apply those tools to solve synthetic challenges * Provides case studies and guided examples with graphical illustrations in each chapter Enabling Tools and Techniques for Organic Synthesis: A Practical Guide to Experimentation, Automation, and Computation is an invaluable reference for scientists needing an up-to-date introduction to new tools, graduate students wanting to expand their organic chemistry skills, and instructors teaching courses in advanced techniques for organic synthesis.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

List of Contributors

Preface

1 Biocatalysis 101 – A Chemist’s Guide to Starting Biocatalysis

1.1 Introduction

1.2 When Should I Choose an Enzyme over a Chemical Catalyst?

1.3 Key Considerations for Running Biocatalytic Reactions

1.4 Transformations Catalyzed by Enzymes

1.5 New Trends and Technologies in Biocatalysis

1.6 Flow Chart to Biocatalysis

1.17 Case Study: Setting up a Biotransformation

1.8 Concluding Remarks

Additional Resources

References

2 Introduction to Photochemistry for the Synthetic Chemist

2.1 Introduction

2.2 How to Plan a Photochemical Synthesis

2.3 Selected Applications of Photochemical/Photocatalyzed Reactions

2.4 Conclusions

Acknowledgment

References

3 How to Confidently Become an Electrosynthetic Practitioner

3.1 Introduction

3.2 General Definition of Organic Electrosynthesis

3.3 Why is Organic Electrosynthesis Used?

3.4 How is Organic Electrosynthesis Performed?

3.5 Where to Start with Electrosynthesis?

3.6 Electrasyn 2.0

3.7 Case Study

3.8 Conclusion

References

4 Flow Chemistry

4.1 Introduction

4.2 General Information for Flow Microreactors

4.3 Case Studies

4.4 Further Expertise

4.5 Summary and Outlook

References

5 Reaction Optimization Using Design of Experiments

5.1 Introduction

5.2 When and How Can DoE Be Used?

5.3 What Information Can I Get from a DoE and How Is It Obtained?

5.4 What Types of Design Are Available?

5.5 The DoE Process

5.6 Combining DoE with Other Screening and Optimization Techniques

5.7 Software

5.8 “I Tried Experimental Design But It Did Not Work”

5.9 Conclusion

References

6 Introduction to High‐Throughput Experimentation (HTE) for the Synthetic Chemist

6.1 What Is HTE?

6.2 Why HTE and What Can It Achieve?

6.3 Practical Considerations and Tools for HTE

6.4 Section Summary and Outlook

6.5 Case Study 1: Development of an HTE Platform for Nickel‐Catalyzed Suzuki–Miyaura Reactions

6.6 Case Study 2: HTE Enabled Reaction Discovery and Optimization of Silyl‐Triflate‐Mediated C–H Aminoalkylation of Azoles

6.7 Current Challenges and the Future of HTE

Acknowledgments

Further Recommended Reading

References

7 Concepts and Practical Aspects of Computational Chemistry

7.1 Introduction

7.2 Hardware and Software Requirements for Computational Investigations

7.3 Typical Methods in Computational Organic Chemistry

7.4 Basis Sets Used in Computational Organic Chemistry

7.5 Typical Computational Tasks in Organic Chemistry

7.6 Notation of the Model Chemistry

7.7 The Diels–Alder Reaction as a Tutorial Case Study

7.8 More Advanced Aspects

7.9 Important and Frequently Used Keywords

7.10 Practical Considerations

7.11 Conclusions

References

8 NMR Prediction with Computational Chemistry

8.1 Introduction

8.2 Quantum‐Chemistry‐Based Computational NMR

8.3 Summary and Outlook

Key References

References

9 Introduction to Programming for the Organic Chemist

9.1 Introduction

9.2 Better Visualizations: Communicating Structure–Data Relationships

9.3 Text Extraction: Automating Density Functional Theory Calculations

9.4 Statistical Analysis: Deriving Insight from Historical Data

9.5 Machine Learning: A Predictive Model for Deoxyfluorination

9.6 Working with Public Datasets: Identifying Reactivity Cliffs

9.7 Running Simulations: Process Greenness

9.8 Application Development: Process Mass Intensity Predictor

9.9 Machine Learning for Reaction Optimization

9.10 Executing Robotic Tasks

9.11 Autonomous Reaction Optimization

9.12 Conclusion

References

10 Machine Learning for the Optimization of Chemical Reaction Conditions

10.1 Introduction

10.2 Prior Art and Alternative Methods for Rational Reaction Optimization

10.3 Reaction Optimization Using LabMate.ML

10.4 Primer on Evaluation Guidelines

10.5 Outlook

References

11 Computer‐Assisted Synthesis Planning

11.1 Introduction to Computer‐Aided Synthesis Planning

11.2 Approaches and Algorithms for Retrosynthesis

11.3 Approaches and Algorithms for Condition Recommendation and Forward Synthesis

11.4 Select Examples of Software Tools for CASP

11.5 Case Studies

11.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Absorption of selected organic compounds.

Chapter 3

Table 3.1 Electrolyte screen.

Table 3.2 Pre‐catalyst screen.

Table 3.3 Ligand screen.

Table 3.4 Solvent screen.

Table 3.5 Additives screen.

Table 3.6 Equivalents screen.

Table 3.7 Current variations.

Table 3.8

F·mol

−

1

screen

.

Table 3.9 Electrode screen.

Chapter 4

Table 4.1 List for equipment for an initial trial.

Table 4.2 Recipe for the reaction of

1

with

n

‐butyl lithium.

Table 4.3 Reaction conditions.

Table 4.4 Typical conditions for changing the flow rates.

Table 4.5 Prepared table in experiment notebook.

Table 4.6 Example of the table in experiment notebook after adding internal ...

Table 4.7 Completed table for the flow reaction.

Table 4.8 Quantities for the reaction of

3

,

n

‐butyl lithium, and methanol.

Table 4.9 Prepared table to summarize the reaction

a

.

Table 4.10 Summary of results for the flow reaction under the conditions sho...

Chapter 5

Table 5.1 Two‐factor design table.

Table 5.2 Results table with factor and interaction codings.

Table 5.3 Resolution of fraction factorial designs and confounding terms.

Table 5.4 Factors and settings for the Wittig reaction.

Table 5.5 Responses, abbreviations, and units for the Wittig design.

Table 5.6 How experiment numbers change with factors and design types.

Table 5.7 Confounded interactions in Wittig design.

Table 5.8 ANOVA table, isolated yield.

Table 5.9 Model statistics summary for solution yield and impurities.

Table 5.10 Additional experiments to complement the design.

Table 5.11 Example response summary table.

Table 5.12 Response summary table for Wittig design.

Chapter 7

Table 7.1 Jacob’s ladder of different types of DFT functionals.

Table 7.2 Crude comparison of the time required to calculate the energy of a...

Table 7.3 Comparison of basis‐set size for selected basis sets within Gaussi...

Table 7.4 Calculated free energies for each conformer of the Diels–Alder rea...

Table 7.5 Calculated free energies for each transition‐state conformer of th...

Table 7.6 Selected Gaussian and ORCA keywords.

Chapter 8

Table 8.1 Computational and experimental

13

C and

1

H NMR results.

Chapter 9

Table 9.1 Validation of simulated PMI ranges.

Table 9.2 Results of Chemputer controlled synthesis.

Chapter 10

Table 10.1 Summary of required evaluation studies according to the manuscrip...

Chapter 11

Table 11.1 A list of closed‐source CASP programs under active commercial dev...

List of Illustrations

Chapter 1

Figure 1.1 A combined enzymatic cascade/hydroxylamination for the synthesis ...

Figure 1.2 The CBS reaction has been used in undergraduate texts as a classi...

Figure 1.3 Proteins are formed by intracellular machinery that uses genetic ...

Figure 1.4 Asymmetric reduction of imines to amines in the presence of a chi...

Figure 1.5 Biocatalytic dynamic kinetic resolutions of aldehydes using imine...

Figure 1.6 Cost comparison of chemical and biological catalysts in USD kg

−1

...

Figure 1.7 Forms that enzymes can be stored/sold in.

Figure 1.8 Standard pieces of equipment needed for running a biocatalytic re...

Figure 1.9 Classification of enzymes based on reaction type with several exa...

Figure 1.10 Generic reaction catalyzed by alcohol dehydrogenase.

Figure 1.11 Interconversion mediated by aspartate aminotransferase.

Figure 1.12 Chemoenzymatic synthesis of (

S 

)‐(

+

)‐Citalopram.

Figure 1.13 Reaction catalyzed by phenylalanine ammonia lyase (PAL).

Figure 1.14 Reaction catalyzed by glucose‐6‐phosphate isomerase.

Figure 1.15 Reaction catalyzed by pyruvate carboxylase.

Figure 1.16 Use of horseradish peroxidase in Western blotting.

Figure 1.17 Reaction catalyzed by lysozyme.

Figure 1.18 Transformation catalyzed by

D

‐amino acid dehydrogenase. Acceptor...

Figure 1.19 Representation of the typical units and general setup for flow c...

Figure 1.20 General enzyme immobilization techniques and their potential app...

Figure 1.21 Two approaches of enzyme engineering.

Figure 1.22 Flow chart to biocatalysis.

Figure 1.23 Enzymatic kinetic resolution of (

R

/

S

 )‐1‐phenylethanol.

Figure 1.24 Sigma Aldrich search results for “Candida Antartica lipase B” (a...

Chapter 2

Figure 2.1 Transitions of selected chromophores.

Scheme 2.1 Selected photochemical reactions of double bonds and carbonyls: (...

Figure 2.2 Electronic transitions occurring in alkenes (π

→

π*) and keto...

Figure 2.3 General scheme describing thermal (in magenta), photochemical (in...

Figure 2.4 Common photocatalysts/photosensitizers used in synthetic applicat...

Figure 2.5 Absorption edge of some commonly used solvents (1 cm optical path...

Figure 2.6 Transparency of materials used for the reaction vessel (1 mm thic...

Figure 2.7 (a) Low‐pressure mercury arcs used for photochemical reactions. F...

Figure 2.8 (a) Parts of an immersion well irradiation apparatus (from left t...

Figure 2.9 Kessil lamp emitting at 427 nm.

Figure 2.10 Reactors using internal (left) and external (right) irradiation....

Scheme 2.2 Deracemization of

α

‐branched aldehydes.

Figure 2.11 (Left) Photoreactor. (Middle) 0.2 mmol scale reaction setup. (Ri...

Scheme 2.3 Photosensitized synthesis of Calcipotriol

8

.

Scheme 2.4 Synthesis of bridged cyclobutanes.

Figure 2.12 Photochemical apparatus employed for the synthesis of

11a‐c.

...

Scheme 2.5 Paternò–Büchi reaction for the construction of the tetrahydrooxet...

Figure 2.13 Photochemical apparatus employed for the preparation of

13a‐e.

...

Scheme 2.6 Atom‐transfer radical addition (ATRA) reaction between an

N

‐chlor...

Figure 2.14 Photochemical flow reactor employed for the synthesis of

18a‐e.

...

Scheme 2.7 Photosensitized synthesis of Artemisinin

20

.

Scheme 2.8 Synthesis of four‐membered rings via photoredox catalyzed generat...

Figure 2.15 Photochemical apparatus employed for the synthesis of

23a‐d.

...

Scheme 2.9 Sunlight‐induced formation of unsymmetrical ketones from aldehyde...

Figure 2.16 Photochemical apparatus employed for the synthesis of

29a‐b.

...

Figure 2.17 Photochemical flow reactor employed for the synthesis of

29a,b.

...

Chapter 3

Figure 3.1 Commercially available ElectraSyn 2.0. (a) Commercial box; (b) Eq...

Figure 3.2 Commercially available accessories.

Figure 3.3 Electrodes.

Figure 3.4 ElectraSyn 2.0.

Figure 3.5 Adjusting voltage limit.

Figure 3.6 Reaction setup.

Figure 3.7 (a) Starting an electrochemical experiment. (b) Starting an elect...

Figure 3.8 During the reaction.

Figure 3.9 Occurrence of dialkyl ketones in bioactive molecules.

Scheme 3.1 (a) Photochemical Ni‐catalyzed formation of dialkyl ketones from ...

Scheme 3.2 From literature precedents to first electrochemical attempt. (a) ...

Scheme 3.3 Optimized conditions for the nickel‐catalyzed electroreductive cr...

Scheme 3.4 Selected scope.

Chapter 4

Figure 4.1 Schematic for simple tube reactor R1 and its variable. Units are ...

Figure 4.2 Schematic for residence time control.

Figure 4.3 Schematic for mixing by multi‐lamination‐type micromixer.

Figure 4.4 Scaling merit for specific surface area.

Figure 4.5 (a) Typical example of a competitive sequential reaction. (b) Sch...

Scheme 4.1 Reaction of

n

‐butyllithium with benzoyl chloride.

Scheme 4.2 Schematic for reaction mediated by unstable intermediate.

Figure 4.6 Theoretical trend of yield and recovery against reaction time in ...

Scheme 4.3 Residence time controlling for the structure of highly reactive i...

Scheme 4.4 Schematic for reaction integration.

Figure 4.7 Reaction integration in (a) batch reactor and (b) flow reactor.

Figure 4.8 Photography of T‐shaped micromixer (Sanko Seiki) (a) and its sche...

Figure 4.9 A tube reactor made of stainless steel.

Figure 4.10 Schematic for a syringe pump.

Figure 4.11 Schematic for the use of PTFE tube and pre‐cooling tube.

Figure 4.12 Schematic for the use of PTFE tube as a connection for two baths...

Figure 4.13 Case study for calculating equivalent amounts.

Figure 4.14 Tips for syringe techniques. (a) Filling the syringe with an ine...

Figure 4.15 Gastight syringe set on syringe pump horizontally.

Scheme 4.5 Schematic for addition of organolithium to trialkyl borate. R and...

Scheme 4.6 Borylation of lithium reagent (model reaction).

Figure 4.16 (a) Equipment used. (b) Reactor assembly. (c) Reactor in cooling...

Figure 4.17 Schematic for the flow reaction. The lengths of the PTFE tube ar...

Scheme 4.7 Generation and reaction of cyano‐substituted aryl lithium in a ba...

Figure 4.18 Schematic for the flow reaction of 4‐bromobenzonitrile with

n

‐bu...

Scheme 4.8 Introduction of two electrophiles to 1,2‐dibromobenzene.

E

1

and

Figure 4.19 Optimization of two aforementioned reactions of the integrated r...

Figure 4.20 Schematic for flow reactor system for the sequential reaction of...

Figure 4.21 Schematic of flow reactors for linear and convergent reaction in...

Chapter 5

Scheme 5.1 Substitution reaction.

Figure 5.1 OVAT screening (top) vs. DoE screening (bottom).

Figure 5.2 Interaction plots.

Figure 5.3 Linear line equation and quadratic line equation.

Figure 5.4 Left, results from initial design. Right, sequential experiments ...

Figure 5.5 Different response surface designs.

Scheme 5.2 A Wittig reaction for optimization using the DoE process.

Scheme 5.3 Potentially significant factors considered for the Wittig reactio...

Figure 5.6 Replicate plots.

Figure 5.7 Overview plot for the model for isolated yield as generated by Mo...

Figure 5.8 Contour plot for the model for yield including ylide

2

(left) or t...

Figure 5.9 Observed vs. predicted plot for the model for isolated yield.

Figure 5.10 Contour plot for the model for solution yield.

Figure 5.11 Interaction plot for the ylide*temperature interaction in the mo...

Figure 5.12 Contour plots for the model for impurities including ylide

2

(lef...

Figure 5.13 Overview plot with all data for the model for isolated yield.

Figure 5.14 Extended contour plot for the model for isolated yield.

Figure 5.15 An example reaction profile showing consumption of starting mate...

Chapter 6

Figure 6.1

Visualization of reaction miniaturization.

Sizes of reaction vess...

Figure 6.2 Visualization of sequential reaction optimization (a) vs. multiva...

Figure 6.3 Visualization of a typical HTE workflow.

Figure 6.4 Visualization of HTE experiment design process, incorporating inf...

Figure 6.5 Two examples of how to translate a design to an HTE array.

Figure 6.6

Example of how to prepare an HTE plate.

(a) Preparation of stock ...

Figure 6.7 Example of how to quench and prepare HTE reactions for analysis....

Figure 6.8 Example of initial hit and follow‐up screens.

Figure 6.9

Example of Excel sheet used for creating an HTE layout (top) and

...

Figure 6.10

Examples of reagent library (top left), plate design and recipe

...

Figure 6.11

Examples of commonly utilized high‐throughput plates.

(a) ...

Figure 6.12

Examples of high pressure HTE blocks from unchained labs

. (a) Se...

Figure 6.13 Small stir bars for 250 μL vials (a) and 1 mL vials (b).

Figure 6.14 Temperature regulated tumble stirrer with a 96‐well plate (left)...

Figure 6.15

Example of consumable items for HTE.

1. Electric screwdriver, 2....

Figure 6.16

Assembling an HTE plate.

(a) Preparation of HTE plate. (b) Illust...

Figure 6.17

KitAlysis

TM

inertion box.

The box is shown with an open lid (can...

Figure 6.18

Illustration of how to prepare a pipette tip for stock slurry di

...

Figure 6.19 Example of available multichannel (a), single channel (c), and r...

Figure 6.20

Solutions for solvent evaporation from HTE plates

: Genevac evapo...

Figure 6.21 Examples of Excel plots visualizing enantiomeric excess (ee) as ...

Figure 6.22 Example of Spotfire plot, visualizing ee (by color) and conversi...

Figure 6.23 Automated balance XPE205 with sample changer (Mettler Toledo).

Scheme 6.1 HTE screening platform for the Ni‐catalyzed Suzuki–Miyaura coupli...

Scheme 6.2

Screening round 1: HTE evaluation of ligand, solvent mixtures, an

...

Scheme 6.3

Screening round 2: Influence of solvent and % MeOH on coupling re

...

Scheme 6.4 HTE platform for Ni‐catalyzed Suzuki–Miyaura reactions.

Scheme 6.5

Second model reaction for validation of Ni‐catalyzed Suzuki–Miyau

...

Scheme 6.6

Demonstration of generality of the developed screening platform b

...

Scheme 6.7 Application and significance of functionalized azole scaffolds.

Scheme 6.8

HTE evaluation of ligand, solvents, and bases for Ni‐catalyzed C–

...

Scheme 6.9

Follow‐up HTE experiment, further elucidating the effect of N,N‐l

...

Scheme 6.10 Multidimensional optimization of reactants.

Scheme 6.11 HTE screen with added exogenous bases.

Scheme 6.12 General overview of developed reaction (a) and proposed mechanis...

Chapter 7

Scheme 7.1 Reaction mechanisms for electrophilic aromatic substitutions.

Figure 7.1 Basis‐set dependence of a molecular property (D (double), T (trip...

Figure 7.2 Comparison of the exponential functions in Slater‐type (green das...

Figure 7.3 Schematic visualization of the influence of polarization function...

Figure 7.4 Flowchart for a typical computational project.

Figure 7.5 Schematic representation of the process of geometry optimization....

Scheme 7.2 The Diels–Alder reaction between 2‐methoxybutadiene (

1

) and acrol...

Scheme 7.3 A typical Gaussian input file (left) with comments (right).

Scheme 7.4 A typical ORCA input file (left) with comments (right).

Scheme 7.5 ChemDraw representations for the potential conformers of the star...

Figure 7.6 Schematic representation of an initial constrained preoptimizatio...

Scheme 7.6 Representative examples for a constrained optimization in Gaussia...

Scheme 7.7 Representative examples for an IRC calculation in Gaussian (left)...

Figure 7.7 Following the Diels–Alder reaction between

1

and

2

with IRC calcu...

Chapter 8

Figure 8.1 Cyclical process of revising structures using computational NMR....

Figure 8.2

1

H and

13

C chemical shift prediction differences between the carbox...

Figure 8.3 Example of a free energy profile from conformational searching. (...

Figure 8.4 Structures of C

16

H

34

conformational snapshots in two 5 ps simulat...

Figure 8.5 Difference in populations of two most populated conformers predic...

Figure 8.6 Improvement in

1

H NMR shift prediction with explicit solvent inclu...

Figure 8.7 C–H–π interaction effect on acidic

1

H chemical shifts in benzene n...

Figure 8.8 Coupling constant predictions.

Figure 8.9 Seven diastereoisomeric structures of hyacinthines assigned using...

Figure 8.10 Schematic of the DP5 program.

Figure 8.11

1

H chemical shift prediction compared to experimental

1

H chemical...

Figure 8.12 The curcusones with stereogenic centers in question marked by as...

Figure 8.13 The structure first reported for acremolin from the natural prod...

Chapter 9

Figure 9.1 Synthetic chemists employ networks of digital equipment and media...

Figure 9.2 Reproducible research and visualization enabled by programming.

Figure 9.3 Radial‐Scope plot example.

Figure 9.4 Options for data transcription.

Figure 9.5 Programmatic workflow for extracting values from Spartan job file...

Figure 9.6 Deriving insight from historical data.

Figure 9.7 Result of statistical analysis of historical crystallization scre...

Figure 9.8 Machine learning for reaction prediction.

Figure 9.9 Dataset production to support machine learning.

Figure 9.10 Predictive performance of the deoxyfluorination machine learning...

Figure 9.11 Accessing public data to support research.

Figure 9.12 Unexpected reactivity for DinoPhos ligands.

Figure 9.13 Observed reactivity cliff at 32% Vbur (min).

Figure 9.14 Programmatic exploration of data for reactivity cliffs.

Figure 9.15 Using simulations for determining likely outcomes.

Figure 9.16 Process definition for PMI predictor app.

Figure 9.17 Interactive range specification for the PMI predictor app.

Figure 9.18 Results from PMI predictor app.

Figure 9.19 Required components for a Bayesian Optimizer.

Scheme 9.1 Reaction employed for a ground‐truth dataset to test the Bayesian...

Figure 9.20 Application developed to play the optimization game against the ...

Scheme 9.2 Examples of Bayesian optimization applied to two chemical reactio...

Figure 9.21 Chemputer information flow.

Figure 9.22 Workflow for autonomous reaction optimization.

Scheme 9.3 Reaction selected for autonomous optimization.

Figure 9.23 Comparison of results using manual expertise, expert‐guided auto...

Chapter 10

Figure 10.1 The underlying concept of reaction condition optimization with m...

Figure 10.2 Schematics of different concepts and approaches for process opti...

Figure 10.3 Schematics of the LabMate.ML workflow for the optimization of re...

Figure 10.4 The initializer.py script enumerates all possible reaction condi...

Figure 10.5 LabMate.ML optimizes reaction conditions using random forests (R...

Figure 10.6 Examples of use cases where LabMate.ML was employed to optimize ...

Figure 10.7 Proposed retrospective workflow for machine learning routines. C...

Figure 10.8 Comparison of LabMate.ML with simpler, linear regression methods...

Figure 10.9 Examples of use cases where machine learning was applied for the...

Chapter 11

Figure 11.1 An example of a precedent reaction and the corresponding algorit...

Figure 11.2 One of the tens of thousands of retrosynthetic templates in the ...

Figure 11.3 Sequential graph edits of the target molecule into reactants. At...

Figure 11.4 Schematic of two‐stage template‐free approach. At the first stag...

Figure 11.5 An illustration of the four phases of Monte Carlo Tree Search: s...

Figure 11.6 A screenshot of the Interactive Path Planner panel for a sample ...

Figure 11.7 Screenshots of the Tree Builder setting page and the generated s...

Figure 11.8 The GUI of AiZynthFinder in the Jupyter environment, whose panel...

Figure 11.9 Sample result of AiZynthFinder in the Jupyter notebook cell when...

Figure 11.10 A sample printout by running the example.py script under Retro*...

Figure 11.11 Examples of molecular targets from the case studies described i...

Guide

Cover Page

Title Page

Copyright Page

List of Contributors

Preface

Table of Contents

Begin Reading

Index

Wiley End User License Agreement

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Enabling Tools and Techniques for Organic Synthesis

A Practical Guide to Experimentation, Automation, and Computation

Edited by

Stephen G. Newman

Department of Chemistry & Biomolecular Sciences

University of Ottawa

Ottawa, Canada

Copyright © 2023 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.Published simultaneously in Canada.

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Library of Congress Cataloging‐in‐Publication Data Applied for

ISBN: 9781119855637, ePDF: 9781119855651, epub:9781119855644, oBook:9781119855668

Cover Design: WileyCover Image: © Sergey Tarasov/Adobe Stock Photos

List of Contributors

Yosuke AshikariDepartment of Chemistry Faculty of ScienceHokkaido University Sapporo, Japan

Martin BreugstInstitut für ChemieTechnische Universität Chemnitz Chemnitz, Germany

Sylvain CharvetInstitut de Chimie et Biochimie Moléculaires et Supramoléculaires (ICMBS, UMR 5246 du CNRS)Université LyonVilleurbanne, France

Connor W. ColeyDepartment of Chemical Engineering Massachusetts Institute of Technology Cambridge, MA, USAandDepartment of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge, MA, USA

Pablo Díaz‐KruikDepartment of Chemistry Biochemistry and Pharmaceutical SciencesBern, Switzerland

Marion H. EmmertProcess Research & Development Merck & Co., Inc.Rahway, NJ, USA

Maurizio FagnoniPhotoGreen LaboratoryDepartment of Chemistry University of PaviaPavia, Italy

Stephanie FeltenProcess Research & Development Merck & Co., Inc.Rahway, NJ, USA

A. Filipa de AlmeidaInstituto de Investigação do Medicamento (iMed)Faculdade de FarmáciaUniversidade de LisboaLisbon, Portugal

Laura ForfarPaul Murray Catalysis Consulting LtdYate, Bristol, UK

Wentao GuoDepartment of Chemistry University of CaliforniaDavis, CA, USA

Taline KerackianInstitut de Chimie et BiochimieMoléculaires et Supramoléculaires (ICMBS, UMR 5246 du CNRS)Université LyonVilleurbanne, France

Itai LevinSynthetic Biology CenterDepartment of Biological Engineering Massachusetts Institute of Technology Cambridge, MA, USAandDepartment of Chemical Engineering Massachusetts Institute of Technology Cambridge, MA, USA

David LimDepartment of ChemistryBiochemistry and Pharmaceutical SciencesBern, Switzerland

Amy T. MerrillDepartment of ChemistryUniversity of California Davis, CA, USA

Paul MurrayPaul Murray Catalysis Consulting LtdYate, Bristol, UK

Aiichiro NagakiDepartment of ChemistryFaculty of ScienceHokkaido UniversitySapporo, Japan

Francesca ParadisiDepartment of ChemistryBiochemistry and Pharmaceutical SciencesBern, Switzerland

Stefano ProttiPhotoGreen LaboratoryDepartment of ChemistryUniversity of PaviaPavia, Italy

Davide RavelliPhotoGreen LaboratoryDepartment of ChemistryUniversity of PaviaPavia, Italy

Tiago RodriguesInstituto de Investigação doMedicamento (iMed)Faculdade de FarmáciaUniversidade de LisboaLisbon, Portugal

Camille Z. RubelInstitut de Chimie et BiochimieMoléculaires et Supramoléculaires (ICMBS, UMR 5246 du CNRS)Université LyonVilleurbanne, FranceandDepartment of ChemistryThe Scripps Research InstituteLa Jolla, CA, USA

Michael ShevlinProcess Research & DevelopmentMerck & Co., Inc.Rahway, NJ, USA

Jason M. StevensBristol Myers SquibbSummit, NJ, USA

Dean J. TantilloDepartment of Chemistry University of CaliforniaDavis, CA, USA

Zhengkai TuComputational Science and EngineeringMassachusetts Institute of TechnologyCambridge, MA, USA

Julien C. VantouroutInstitut de Chimie et BiochimieMoléculaires et Supramoléculaires (ICMBS, UMR 5246 du CNRS)Université LyonVilleurbanne, France

Preface

At the undergraduate level, the organic chemistry curriculum at most universities is similar. Professors emphasize the fundamental concepts necessary to understand how, when, and why organic molecules interact, while lab instructors familiarize students with important hands‐on aspects of carrying out experiments. Bachelor’s students can expect to finish their studies with an idea of how molecules behave, how they are made, and how technologies such as NMR and IR can be used for their characterization. Those that enter graduate school are often surprised at the breadth of powerful technologies that make advanced organic chemistry the discipline it is today. Instead of a typical stirred round‐bottomed flask, many reactions are better done using photochemical, electrochemical, or flow reactors. Computational chemistry, once reserved for dedicated experts, can now be used by organic chemists to help predict outcomes, understand selectivity, and decipher reaction mechanisms. Automation technology can be used to generate large amounts of data with limited amounts of material, and data processing software can be used to extract subtle trends.

Due to their prominence in the recent literature, trainees and established chemists alike would benefit from gaining expertise with these technologies to be best prepared for solving the diverse synthetic challenges that come their way. However, the barrier to learning techniques without formal instruction can be high. Even if one is fortunate enough to have access to advanced training, the expert instructor may not necessarily curate the course to the needs and the background of a synthetic chemist. The primary literature and recent textbooks have similar limitations – while there is no shortage of resources, experts generally write to other experts, and the interested organic chemist may have critical gaps in their understanding and struggle with subdiscipline‐specific jargon.

The goal of this text is to help fill this gap by providing synthetic chemists with a user‐friendly starting point to initiate their journey in developing new skills and knowledge. In each of the 11 chapters, experts communicate basic information about an impactful technology in a manner accessible to a classically trained synthetic chemist. Chapters also includes a glossary of common terminology, a general introduction to the technology of interest, case‐study examples of how it may useful to synthetic chemists, a practical discussion about steps one may take to put knowledge into practice, and references to recommended further reading. The book seeks to be a go‐to resource for organic chemists at or above the graduate level that wish to expand the breadth of tools they can use to perform, analyze, and interpret chemistry experiments. After completion, the reader will be armed with the practical knowledge needed to comprehend the literature, to assess the strengths and limitations of each technique, and to begin applying modern tools to solve synthetic challenges. This will make it useful as a general resource for graduate students looking to expand their expertise, for instructors of graduate‐level courses on advanced techniques for organic synthesis, and for industrial scientists seeking a beginner‐friendly way to expand their knowledge.

The book is organized into four subsections. Chapters 1–4 describe different enabling technologies for performing chemical experiments – biocatalysis, photochemistry, electrochemistry, and flow chemistry. While none of these topics are fundamentally new, their power as a tool for organic synthesis is becoming increasingly evident. These chapters will help the reader overcome the technical barrier hindering them from comfortably replicating experiments and designing their own. Chapters 5 and 6 focus on improved approaches to select, carry out, and analyze experiments. Specifically, Chapter 5 describes a statistical approach to experimentation that can be used to understand and optimize chemical reactions. This Design of Experiments (DoE) technique is commonly employed by practicing scientists in many fields but is seldom taught to chemists. Chapter 6 describes techniques that researchers can use to get more data using less time and fewer resources. This high‐throughput experimentation (HTE) approach shows the reader how to carry out reactions in parallel and how the collected data can be interpreted to gain insights that might otherwise be missed. Chapters 7 and 8 introduce the reader to computational chemistry tools that enable molecules and reactions to be modeled in silico, providing predictions and mechanistic insight to supplement experimentation. Chapter 7 provides a general overview of the most common computational tasks that an organic chemist may want to carry out and walks the reader through a beginner‐friendly case study wherein the reactants, transition states, and products of a Diels–Alder reaction are calculated. Chapter 8 builds upon the general knowledge given in the previous chapter and describes how computational chemistry can be used to predict the NMR spectrum of organic molecules. The goal of this chapter is to put this powerful technique into the hands of experimental chemists, which should be achievable after familiarizing themselves with the simplified approach detailed throughout. Chapters 9–11 provide the reader with an introduction to programming and machine learning. Computers already play a critical role in the daily life of a synthetic chemist, and a little bit of familiarity with modern techniques can go a long way. Chapter 9 provides a blueprint for understanding how and why a chemist may go about familiarizing themselves with programming. Chapter 10 describes a deep dive case study for using machine learning to facilitate reaction optimization, providing a step‐by‐step guide that a beginner may follow to use the tool and to gain confidence in harnessing other published algorithms. Chapter 11 explains how computers can facilitate the planning of multistep synthesis by suggesting synthetic routes and reaction conditions. Helpful discussions on the current tools available, how they work, and their associated strengths and weaknesses are also described.

This project was only possible due to an immense amount of work by the authors who generously agreed to share their knowledge and meet the formidable task of communicating with a general audience. I am also indebted to the many students and postdoctoral fellows at the University of Ottawa that served as reviewers to help ensure that the content serves as a welcoming and beginner‐friendly introduction to these topics that are becoming increasingly important to the modern synthetic chemists. I hope the readers agree that this goal has been met and that this marks the beginning of their journey to being a more well‐rounded scientist capable of tackling diverse problems that come their way.

Stephen G. Newman

Ottawa

July 2023