دانلود کتاب Enabling Tools and Techniques for Organic Synthesis: A Practical Guide to Experimentation, Automation, and Computation

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Enabling Tools and Techniques for Organic Synthesis: A Practical Guide to Experimentation, Automation, and Computation
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Contents
List of Contributors
Preface
1. Biocatalysis 101 – A Chemist’s Guide to Starting Biocatalysis
Glossary
1.1 Introduction
1.1.1 Enzymes – the Green and Sustainable Way of the Future
1.1.2 Enzymatic and Organic Catalysis Are Not too Different from Each Other
1.1.3 Enzymes 101
1.2 When Should I Choose an Enzyme over a Chemical Catalyst?
1.3 Key Considerations for Running Biocatalytic Reactions
1.3.1 Dispelling Myths
1.3.1.1 Enzymes Are Not Safe to Use
1.3.1.2 Enzymes Are Not as Readily Available as Chemical Catalysts
1.3.1.3 Enzymes Are Seldom Useful Due to Their Limited Substrate Scope
1.3.1.4 The Cost of Enzyme Production Is Very High
1.3.1.5 Enzymes Are Functionally Unstable Under Organic Conditions
1.3.1.6 Sustainability
1.3.2 Challenges of Using Enzymes: the Need for Strict Reaction Conditions
1.3.2.1 Enzymes from Extremophiles
1.3.2.2 Solvents (and Co-solvents)
1.3.2.3 Concentration and Ionic Strength of the Buffer
1.3.2.4 pH Dependence
1.3.2.5 Concentration of Reactants
1.3.2.6 Enzyme Concentration
1.3.2.7 Enzyme Forms
1.3.2.8 Toxicity
1.3.3 What Do I Need to Start Biocatalytic Experiments in My Lab?
1.3.4 Additional Considerations
1.4 Transformations Catalyzed by Enzymes
1.4.1 EC – The Enzyme Commission Number
1.4.1.1 EC 1 – Oxoreductases
1.4.1.2 EC 2 – Transferases
1.4.1.3 EC 3 – Hydrolases
1.4.1.4 EC 4 – Lyases
1.4.1.5 EC 5 – Isomerases
1.4.1.6 EC 6 – Ligases
1.4.1.7 EC 7 – Translocases
1.4.2 Some Applications of Selected Commercially Available Enzymes
1.4.2.1 Horseradish Peroxidase
1.4.2.2 Lysozyme
1.4.2.3 Trypsin
1.4.2.4 Candida Lipase B
1.4.2.5 Amino Acid Dehydrogenase
1.4.2.6 Glycosidases
1.4.3 Engineered (Unnatural) Reactions
1.5 New Trends and Technologies in Biocatalysis
1.5.1 Flow Biocatalysis and New Technologies
1.5.1.1 What Is Flow Biocatalysis?
1.5.1.2 How Does Flow Biocatalysis Work?
1.5.1.3 When Is a Flow Process More Beneficial for a Specific Transformation?
1.5.1.4 Should One Implement Every Enzymatic Reaction in Flow?
1.5.2 Enzyme Engineering
1.5.3 Photobiocatalysis
1.6 Flow Chart to Biocatalysis
1.7 Case Study: Setting up a Biotransformation
1.8 Concluding Remarks
References
2. Introduction to Photochemistry for the Synthetic Chemist
Glossary
2.1 Introduction
2.1.1 Light to Make Your Synthesis Greener
2.1.2 A Way to Overcome HOMO/LUMO Interactions
2.2 How to Plan a Photochemical Synthesis
2.2.1 The Choice of the Solvent
2.2.2 Concentration of the Absorbing Species
2.2.3 The Reaction Vessel
2.2.4 Light Sources
2.2.4.1 Low-Pressure
2.2.4.2 Medium-and
2.2.4.3 Other Light Sources
2.2.5 From Batch to Flow Conditions
2.2.6 Preparation of the Sample
2.2.7 Safety Equipment
2.3 Selected Applications of Photochemical/Photocatalyzed Reactions
2.3.1 Reactions Involving the CC Double Bond
2.3.2 Reactions Involving the CO Double Bond
2.3.3 Reactions Involving a Photoinduced Homolysis
2.3.4 Reactions Involving Singlet Oxygen
2.3.5 Reactions Involving a Photocatalytic Step
2.4 Conclusions
Acknowledgment
References
3. How to Confidently Become an Electrosynthetic Practitioner
Glossary
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?
Selected General Reviews
Selected General Guides
3.6 Electrasyn 2.0
3.6.1 Machine and Consumables
3.6.1.1 Opening the IKA ElectraSyn 2.0 Box
3.6.1.2 Cell (Vial and Cap)
3.6.1.3 Electrodes
3.6.2 Interface
3.6.2.1 Hardware
3.6.2.2 Menus
3.6.3 How to Set Up the Cell
3.6.4 How to Start an Experiment
3.6.5 During the Reaction
3.6.6 After the Reaction
3.7 Case Study
3.7.1 Project Overview
3.7.2 Optimization of Parameters
3.7.2.1 Designing an Electrochemical Experiment
3.7.3 Proof of Concept
3.7.3.1 Optimization
3.7.3.2 Substrate Scope
3.8 Conclusion
References
4. Flow Chemistry
Glossary
4.1 Introduction
4.1.1 What is Flow Microchemistry
4.1.1.1 Reaction Time Controllability
4.1.1.2 Fast Mixing
4.1.1.3 Temperature Controllability
4.1.2 Reactions Enabled by Flow Microreactors
4.1.2.1 Competitive Sequential Reactions
4.1.2.2 Reactions Mediated by Unstable Intermediates
4.1.2.3 Reactions Occurring at the Surface: Two-Phase Reactions, Electrochemical Reactions, and Photoreactions
4.1.3 Further Applicability of Flow Microsynthesis
4.1.3.1 Scalability
4.1.3.2 Safety Operation
4.2 General Information for Flow Microreactors
4.2.1 Tools and Equipment for Flow Chemistry
4.2.1.1 Micromixer
4.2.1.2 Tube Reactor
4.2.1.3 Pump
4.2.1.4 Pre-Cooling
4.2.1.5 PTFE Tubes
4.2.2 How to Perform Experiments
4.2.2.1 Selection of Reaction Conditions
4.2.2.2 Preparation of Reagent Solution
4.2.2.3 Preparation for Reactions
4.2.2.4 Preparation for Reaction Evaluation
4.2.2.5 Cleaning Up
4.3 Case Studies
4.3.1 Competitive Sequential Reaction (General Procedure)
4.3.1.1 Preparation
4.3.1.2 Experiment
4.3.1.3 Screening of Reaction Conditions
4.3.1.4 Analysis
4.3.1.5 Clean Up
4.3.2 Reactions Mediated by Short-Lived Intermediates
4.3.3 Reaction Integration
4.4 Further Expertise
4.4.1 Reaction Integration
4.4.2 Chemoselective Reactions
4.4.3 Heterogeneous Catalytic Reactions
4.5 Summary and Outlook
References
5. Reaction Optimization Using Design of Experiments
Glossary
5.1 Introduction
5.1.1 How Do We Experiment and DoE Terminology
5.1.2 OVAT vs. DoE
5.1.2.1 A Simple Chemical Example
5.1.3 A Note on Error, Accuracy, and Precision
5.2 When and How Can DoE Be Used?
5.3 What Information Can I Get from a DoE and How Is It Obtained?
5.3.1 Which Factors Are Important?
5.3.2 How Are the Models Generated?
5.4 What Types of Design Are Available?
5.4.1 Screening Designs
5.4.1.1 Fractional Factorial Designs
5.4.1.2 Definitive Screening Designs
5.4.2 Designs for Optimizing Reactions
5.4.3 Response Surface Designs
5.5 The DoE Process
5.5.1 Aim and Objective
5.5.2 Selecting Factors and Ranges
5.5.2.1 Factors
5.5.2.2 Ranges
5.5.3 Selecting Responses
5.5.4 Select a Design to Answer the Objective
5.5.5 Carry Out Design and Analyze Samples
5.5.6 Check Results
5.5.7 Model Data
5.5.7.1 General Steps for Developing a Model
5.5.7.2 Wittig Reaction
5.5.7.3 Complementing the Design
5.5.8 Validate Predictions
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
Glossary
6.1 What Is HTE?
6.2 Why HTE and What Can It Achieve?
6.2.1 Commonly Perceived Barriers to Employing HTE in Synthetic Chemistry
6.2.1.1 Cost
6.2.1.2 Availability of Dedicated HTE Facilities
6.2.1.3 Access to Knowledge and Training
6.2.1.4 Perception of HTE as Antithesis of Hypothesis-driven
6.2.2 Advantages of HTE Workflows vs. Traditional Reaction Setup
6.2.2.1 Setup Time per Reaction
6.2.2.2 Miniaturization and Efficient Reagent Use
6.2.2.3 Multivariable vs. Sequential Optimization
6.2.2.4 Visualizing Reactivity Patterns
6.2.2.5 Serendipity in Reaction Discovery
6.2.2.6 Avoiding Cross-contamination
6.3 Practical Considerations and Tools for HTE
6.3.1 Outline of a Typical HTE Workflow
6.3.2 Types of HTE Designs
6.3.2.1 HTE for Reaction Discovery
6.3.2.2 HTE for Reaction Optimization
6.3.3 HTE Design Software: Tools for Building Arrays
6.3.4 HTE Reactors and Consumables
6.3.4.1 Reaction Blocks
6.3.4.2 HTE Vials
6.3.4.3 Reaction Blocks with Sealing Top Plate
6.3.4.4 Special Reactors for Photochemistry, Electrochemistry, and High-Pressure
6.3.4.5 Reaction Stirring and Temperature Control
6.3.4.6 Consumables
6.3.5 Considerations for Experimental Setup
6.3.5.1 Reaction Atmosphere
6.3.5.2 Reagent Preparation and Dispensing
6.3.5.3 Storage of Preplated Reagents
6.3.5.4 Pipetting
6.3.5.5 Solvent Evaporation
6.3.6 Analysis of HTE Screens
6.3.6.1 Suitable Instrumentation
6.3.6.2 Autosampler Configurations
6.3.6.3 Analytical Methods
6.3.6.4 Internal Standards and Assay Yields
6.3.6.5 Data Visualization and Analysis
6.3.7 The Role of Automation and Robotics in HTE
6.4 Section Summary and Outlook
6.5 Case Study 1: Development of an HTE Platform for Nickel-Catalyzed Suzuki–Miyaura Reactions
6.5.1 Motivation
6.5.2 Design of Test Reaction and Initial Ligand Screen
6.5.3 Second Round of Ligand/Base/Solvent Screens
6.5.4 Final Platform Design
6.5.5 Validation of Platform Design
6.6 Case Study 2: HTE Enabled Reaction Discovery and Optimization of Silyl-Triflate-Mediated C–H Aminoalkylation of Azoles
6.6.1 Motivation
6.6.2 Reaction Discovery Plate Design
6.6.3 Ligand Screen
6.6.4 Parallel Optimization of Three Reagents
6.6.5 Base Screen
6.7 Current Challenges and the Future of HTE
6.7.1 Summary and Conclusions
6.7.2 Remaining Challenges: The Next Frontiers
6.7.2.1 Biphasic Reaction Mixtures
6.7.2.2 Flow Chemistry and HTE
6.7.2.3 Reaction Profiling
6.7.2.4 Building Machine Learning Models to Predict Reactivity
6.7.2.5 Addressing Future Challenges
Acknowledgments
Further Recommended Reading
References
7. Concepts and Practical Aspects of Computational Chemistry
Glossary
7.1 Introduction
7.2 Hardware and Software Requirements for Computational Investigations
7.3 Typical Methods in Computational Organic Chemistry
7.3.1 General Aspects
7.3.2 Molecular Mechanics and Force Fields
7.3.3 Wave-Function Methods I – Hartree–Fock Theory
7.3.4 Wave-Function Methods II – Post-Hartree–Fock Theory
7.3.5 Semiempirical Methods
7.3.6 Density Functional Theory
7.3.7 Dispersion-Corrected Density Functional Theory
7.3.8 Typical Computational Times
7.4 Basis Sets Used in Computational Organic Chemistry
7.4.1 General Aspects of Basis Sets
7.4.2 Introduction to the Mathematical Formalism in Basis Sets
7.4.3 Polarization and Diffuse Functions
7.4.4 Basis Set Families
7.4.5 Effective Core Potentials (Pseudopotentials)
7.4.6 The Basis Set Superposition Error (BSSE)
7.5 Typical Computational Tasks in Organic Chemistry
7.5.1 Preliminary Remarks
7.5.2 Single-Point Calculations
7.5.3 Geometry Optimizations
7.5.4 Frequency Calculations
7.5.5 Intrinsic Reaction Coordinate (IRC) Calculations
7.5.6 Conformational Analysis
7.6 Notation of the Model Chemistry
7.7 The Diels–Alder Reaction as a Tutorial Case Study
7.7.1 General Aspects and Requirements
7.7.2 Preparing Input Files
7.7.3 Conformational Sampling – Generation of Initial Geometries
7.7.4 Geometry Optimizations of Starting Materials and Products
7.7.5 Locating the Transition States
7.7.6 Verifying the Nature of the Transition State
7.8 More Advanced Aspects
7.8.1 General Comments
7.8.2 Influence of Solvation
7.8.3 Integration Grid
7.8.4 Standard States
7.8.5 Treating Unpaired Electrons
7.9 Important and Frequently Used Keywords
7.10 Practical Considerations
7.11 Conclusions
References
8. NMR Prediction with Computational Chemistry
Glossary
8.1 Introduction
8.2 Quantum-Chemistry-Based Computational NMR
8.2.1 Methods
8.2.1.1 Time/Resources for Calculations
8.2.1.2 Structural Considerations in Modeling
8.2.1.3 Geometry Optimizations
8.2.1.3.1 Level of Theory
8.2.1.4 Calculating Isotropic Shielding Constants
8.2.1.5 Common Pitfalls and How to Address Them
8.2.1.6 Converting to Chemical Shifts
8.2.1.7 Calculating Coupling Constants
8.2.2 Confidence Analysis
8.2.3 Computer-Aided Automated Approaches
8.2.3.1 CASE
8.2.4 A Case Study
8.2.5 Practicing 1H and 13C Chemical Shift Prediction
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
Glossary
10.1 Introduction
10.2 Prior Art and Alternative Methods for Rational Reaction Optimization
10.3 Reaction Optimization Using LabMate.ML
10.3.1 Step One: Accessing the LabMate.ML Code and Installation
10.3.2 Step Two: Initializing the Optimization Routine in LabMate.ML
10.3.3 Step Three: Iterative Optimization Routine
10.3.4 Examples
10.4 Primer on Evaluation Guidelines
10.4.1 Code and Dataset Availability
10.4.2 Retrospective Evaluation
10.4.3 Baselines and Comparing Tools
10.4.4 Prospective Evaluation
10.5 Outlook
References
11. Computer-Assisted Synthesis Planning
Glossary
11.1 Introduction to Computer-Aided Synthesis Planning
11.1.1 Defining the Tasks and Use Cases
11.1.2 Historical Approaches to Computer-Aided Synthesis Planning
11.1.3 The Inflection Point of CASP Methods
11.1.4 Preliminaries on Molecular Representation and Cheminformatics
11.1.5 Outline of the Rest of the Chapter
11.2 Approaches and Algorithms for Retrosynthesis
11.2.1 Data-driven v. Expert-Driven Programs
11.2.2 Template-Based Approaches
11.2.3 Template-free Approaches with Graphs and Sequences
11.2.4 Multistep Planning Algorithms
11.3 Approaches and Algorithms for Condition Recommendation and Forward Synthesis
11.3.1 Condition Recommendation Approaches
11.3.2 Forward Synthesis Approaches
11.4 Select Examples of Software Tools for CASP
11.4.1 Open-Source Tools
11.4.1.1 ASKCOS
11.4.1.2 AiZynthFinder
11.4.1.3 Retro*
11.4.2 Closed-Source Tools
11.4.3 CASP Tools for Enzymatic Catalysis
11.4.4 Practical Considerations for CASP Programs
11.4.4.1 Traceability to Literature Precedent
11.4.4.2 How to Use CASP: Command Line Versus Graphical User Interface
11.4.4.3 Data Privacy
11.4.4.4 Customization Ability
11.5 Case Studies
11.5.1 Segler et al.’s Data-driven Program and A/B Testing Success
11.5.2 MIT’s ASKCOS Program and Robotic Synthesis Demonstration
11.5.3 Grzybowski’s Chematica/Synthia Program’s Experimental Validations and Acquisition
11.6 Conclusion
Key References
References
Index