Computational Drug Discovery: Methods and Applications

fmatter
	Title Page
	Copyright
	Contents
	Preface
	Acknowledgments
	About the Editors
ch1
	Chapter 1 Binding Free Energy Calculations in Drug Discovery
		1.1 Introduction
			1.1.1 Free Energy and Thermodynamic Cycles
		1.2 Endpoint Methods
			1.2.1 MM/PBSA and MM/GBSA
			1.2.2 Linear Response Approximations
		1.3 Alchemical Methods
			1.3.1 Free Energy Perturbation
			1.3.2 Thermodynamic Integration
			1.3.3 Bennett\'s Acceptance Ratio
			1.3.4 Nonequilibrium Methods
			1.3.5 Multiple Compounds
			1.3.6 One‐Step Perturbation Approaches
			1.3.7 Challenges in Alchemical Free Energy Calculations
		1.4 Pathway Methods
		1.5 Final Thoughts
		References
ch2
	2.1 Introduction
	2.2 Methods
		2.2.1 Gaussian Accelerated Molecular Dynamics
		2.2.2 Ligand Gaussian Accelerated Molecular Dynamics
		2.2.3 Energetic Reweighting of GaMD for Free Energy Calculations
		2.2.4 GLOW: A Workflow Integrating Gaussian Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling
		2.2.5 Binding Kinetics Obtained from Reweighting of GaMD Simulations
		2.2.6 Gaussian Accelerated Molecular Dynamics Implementations and Software
	2.3 Applications
		2.3.1 G‐Protein‐Coupled Receptors
			2.3.1.1 Characterizing the Binding and Unbinding of Caffeine in Human Adenosine A2A Receptor
			2.3.1.2 Unraveling the Allosteric Modulation of Human A1 Adenosine Receptor
			2.3.1.3 Ensemble Based Virtual Screening of Allosteric Modulators of Human A1 Adenosine Receptor
		2.3.2 Nucleic Acids
			2.3.2.1 Exploring the Binding of Risdiplam Splicing Drug Analog to Single‐Stranded RNA
			2.3.2.2 Uncovering the Binding of RNA to a Musashi RNA‐Binding Protein
		2.3.3 Human Angiotensin‐Converting Enzyme 2 Receptor
		2.3.4 Discovery of Novel Small‐Molecule Calcium Sensitizers for Cardiac Troponin C
		2.3.5 Binding Kinetics Prediction from GaMD Simulations
	2.4 Conclusions
	References
ch3
	3.1 Introduction
		3.1.1 Preface
		3.1.2 Motivation for Predicting (Un)binding Kinetics
		3.1.3 The Time Scale Problem of MD Simulations
	3.2 Theory of Molecular Kinetics Calculation
		3.2.1 Nonequilibrium Statistical Mechanics in a Nutshell
		3.2.2 Kramers Rate Theory
		3.2.3 Biased MD Methods
			3.2.3.1 Temperature‐ and Barrier‐Scaling
			3.2.3.2 Bias Potential‐Based Methods
			3.2.3.3 Bias Force‐Based Methods
			3.2.3.4 Knowledge‐Biased Methods
			3.2.3.5 Coarse‐graining and Master Equation Approaches
	3.3 Challenges and Caveats in Rate Prediction
		3.3.1 Finding Reaction Coordinates and Pathways
		3.3.2 Error Ranges of Estimates
		3.3.3 A Need for Reliable Benchmarking Systems
		3.3.4 Problems with Force Fields
	3.4 Methods for Rate Prediction
		3.4.1 Unbinding Rate Prediction
			3.4.1.1 Empirical Predictions
			3.4.1.2 Prediction of Absolute Unbinding Rates
		3.4.2 Binding Rate Prediction
	3.5 State‐of‐the‐Art in Understanding Kinetics
	3.6 Conclusion
	References
ch4
	4.1 Introduction
		4.1.1 Protein Folding
		4.1.2 Protein–Ligand Interactions
	4.2 Tools to Assess the Solvation Thermodynamics
		4.2.1 Watermap
		4.2.2 GIST
		4.2.3 3D‐RISM
	4.3 Case Studies
		4.3.1 Watermap
			4.3.1.1 Background and Approach
			4.3.1.2 Results and Discussion
		4.3.2 Grid Inhomogeneous Solvation Theory (GIST)
			4.3.2.1 Objective and Approach
			4.3.2.2 Results and Discussion
		4.3.3 Three‐Dimensional Reference Interaction‐Site Model (3D‐RISM)
			4.3.3.1 Objective and Background
			4.3.3.2 Results and Discussion
	4.4 Conclusion
	References
ch5
	5.1 Introduction
	5.2 SILCS: Site Identification by Ligand Competitive Saturation
	5.3 SILCS Case Studies: Bovine Serum Albumin and Pembrolizumab
		5.3.1 SILCS Simulations
		5.3.2 FragMap Construction
		5.3.3 SILCS‐MC
		5.3.4 SILCS‐Hotspots
		5.3.5 SILCS‐PPI
		5.3.6 SILCS‐Biologics
	5.4 Conclusion
	Conflict of Interest
	Acknowledgments
	References
ch6
	Chapter 6 QM/MM for Structure‐Based Drug Design: Techniques and Applications
		6.1 Introduction
		6.2 QM/MM Approaches
			6.2.1 Combined Quantum Mechanical/Molecular Mechanical Energy Calculations
			6.2.2 QM/MM Methods for the Evaluation of Non‐Covalent Inhibitor Binding
			6.2.3 QM/MM Reaction Modeling
		6.3 Applications of QM/MM for Covalent Drug Design and Evaluation
			6.3.1 Covalent Tyrosine Kinase Inhibitors for Cancer Treatment
			6.3.2 Evaluation of Antibiotic Resistance Conferred by β‐Lactamases
			6.3.3 Covalent SARS‐CoV‐2 Inhibitors: Mechanism and Insights for Design
		6.4 Conclusions and Outlook
		References
ch7
	7.1 Introduction
	7.2 Feasibility of Routine and Fast QM‐Driven X‐Ray Refinement
	7.3 Metrics to Measure Improvement
		7.3.1 Ligand Strain Energy
		7.3.2 ZDD of Difference Density
		7.3.3 Overall Crystallographic Structure Quality Metrics: MolProbity Score and Clashscore
	7.4 QM Region Refinement
	7.5 ONIOM Refinement
	7.6 XModeScore: Distinguish Protomers, Tautomers, Flip States, and Docked Ligand Poses
	7.7 Impact of the QM‐Driven Refinement on Protein–Ligand Affinity Prediction
		7.7.1 Impact of Structure Inspection and Modification
		7.7.2 Impact of Selecting Protomer States: Implications of XModeScore on SBDD
	7.8 Conclusion
	Acknowledgments
	References
ch8
	8.1 Introduction
	8.2 Introduction to FMO
	8.3 Pair Energy Decomposition Analysis (PIEDA)
		8.3.1 Formulation of PIEDA
		8.3.2 Applications of PIEs and PIEDA
		8.3.3 Example of PIEDA
	8.4 Partition Analysis (PA)
		8.4.1 Formulation of PA
		8.4.2 Applications and an Example of PA
	8.5 Partition Analysis of Vibrational Energy (PAVE)
		8.5.1 Formulation of PAVE
		8.5.2 Applications of PAVE
	8.6 Subsystem Analysis (SA)
		8.6.1 Formulation of SA
		8.6.2 Examples of SA and PAVE
	8.7 Fluctuation Analysis (FA)
	8.8 Free Energy Decomposition Analysis (FEDA)
	8.9 Other Analyses of Chemical Reactions
	8.10 Conclusions
	References
ch9
	Chapter 9 The Role of Computer‐Aided Drug Design in Drug Discovery
		9.1 Introduction to Drug–Target Interactions, Hit Identification
		9.2 Lead Identification and Optimization: QSAR and Docking‐Based Approaches
		9.3 DTI Machine Learning Methods
		9.4 Supervised, Non‐supervised and Semi‐supervised Learning Methods
		9.5 Graph‐Based Methods to Label Data for DTI Prediction
		9.6 The Importance of Explainable ML Methods: Linking Molecular Properties to Effects
		9.7 Predicting Therapeutic Responses
		9.8 ADMET‐tox Prediction
		9.9 Challenging Aspects of Using Computational Methods in Drug Discovery
			9.9.1 What are Those Limitations?
		References
ch10
	10.1 Introduction
	10.2 Impact of AI‐Based Protein Models in Structural Biology
		10.2.1 Combination of AI‐Based Predictions with Cryo‐EM and X‐Ray Crystallography
		10.2.2 Combination of AI‐Based Predictions with NMR Structures
		10.2.3 Combination of AI‐Based Predictions with Other Experimental Restraints
		10.2.4 Impact of Deep Learning Models in Other Areas of Structural Biology
	10.3 Combination of AI‐Based Methods with Computational Approaches
		10.3.1 Combination of Structure Prediction with Other Computational Approaches
	10.4 Current Challenges and Opportunities
	10.5 Conclusions
	References
ch11
	11.1 Introduction
	11.2 Deep Learning Models for Reasoning About Protein–Ligand Complexes
		11.2.1 Datasets
		11.2.2 Convolutional Neural Networks
			11.2.2.1 Background
			11.2.2.2 Voxelized Grid Representation
			11.2.2.3 Descriptors
			11.2.2.4 Applications
		11.2.3 Graph Neural Networks
			11.2.3.1 Background
			11.2.3.2 Graph Representation
			11.2.3.3 Descriptors
			11.2.3.4 Applications
			11.2.3.5 Extension to Attention Based Models
			11.2.3.6 Geometric Deep Learning and Other Approaches
	11.3 Deep Learning Approaches Around Molecular Dynamics Simulations
		11.3.1 Enhanced Sampling
		11.3.2 Physics‐inspired Neural Networks
		11.3.3 Modeling Dynamics
			11.3.3.1 Applications
	11.4 Modifying AlphaFold2 for Binding Affinity Prediction
		11.4.1 Modifying AlphaFold2 Input Protein Database for Accurate Free Energy Predictions
		11.4.2 Modifying Multiple Sequence Alignment for AlphaFold2‐Based Docking
	11.5 Conclusion
		11.5.1 New Models for Binding Affinity Prediction
		11.5.2 Retrospective from the Compute Industry
			11.5.2.1 Future DL‐Based Binding Affinity Computation will Require Massive Scalability
			11.5.2.2 Single GPU Optimizations for DL
			11.5.2.3 Distributed DL Training and Inference
	References
ch12
	12.1 Introduction
		12.1.1 Traditional Drug Design and Discovery Process Is Slow and Expensive
		12.1.2 Success and Limitations of Standard Computational Methods
		12.1.3 AI‐Based Methods can Accelerate Medicinal Chemistry
	12.2 Quantitative Structure‐Activity Relationship Models
		12.2.1 Introduction to QSAR Models
		12.2.2 QSAR Machine Learning Methods
		12.2.3 QSAR Deep Neural Networks Methods
	12.3 Modes of Generative AI in Chemistry
		12.3.1 General Introduction
		12.3.2 Generative AI in Lead Optimization
		12.3.3 Fragment Growing
		12.3.4 Novelty Generation
			12.3.4.1 The Model
			12.3.4.2 Optimization of the Novelty Generator
	12.4 Importance of Synthetic Accessibility
		12.4.1 Overview
		12.4.2 Synthetic Scores
		12.4.3 Integration of Synthetic Scores in Generative AI
			12.4.3.1 An Example of a Lead Optimization Use Case
	12.5 The Road Ahead
	References
ch13
	13.1 Introduction
	13.2 Challenges for Modeling
	13.3 Example 1: BBB Applicability Domain Comparison
	13.4 Example 2: Models for Uncertainty Estimation for Multitask Toxicity Predictions
	13.5 Example 3: Class‐Conditional Conformal Predictors
	13.6 Conclusions
	Funding
	Competing Interests
	References
ch14
	Chapter 14 Enumerable Libraries and Accessible Chemical Space in Drug Discovery
		14.1 Chemical Space and Its Generation
			14.1.1 Simple SMILES Enumeration and String Operations
			14.1.2 R‐Group and Scaffold Enumeration
			14.1.3 Bioisosteres and Matched Molecular Pairs
			14.1.4 Enumeration Within De Novo Design
		14.2 Public and Commercial Chemical Libraries
			14.2.1 Vendor Libraries
			14.2.2 Reaction‐Based Chemical Spaces
				14.2.2.1 Reaction Sources
				14.2.2.2 Available Building Blocks
		14.3 How to Effectively Explore Chemical Space?
		References
ch15
	15.1 Introduction to Chemical Space
	15.2 Workflows on the Chemical Space and Related Challenges
		15.2.1 Project Data Exploration
		15.2.2 Corporate Data Warehouses
		15.2.3 Novel Compound Design
		15.2.4 Registration Systems
		15.2.5 SAR‐by Catalog
	15.3 Challenges in Chemical Data Search
	15.4 Technologies
		15.4.1 Representation Used for Similarity Search
			15.4.1.1 Substructure‐Preserving Fingerprints
			15.4.1.2 Feature Fingerprints
		15.4.2 Additional Representations
		15.4.3 Commonly Used Similarity Definitions
	15.5 Similarity Search Applications
	15.6 Substructure Search
		15.6.1 Atom by Atom Search
		15.6.2 Ullmann Algorithm
		15.6.3 VF2 Algorithm
	15.7 Application Example
	15.8 Summary and Outlook
	Acknowledgments
	References
ch16
	16.1 Introduction
	16.2 Exploiting Bioactivity Data in the Artificial Intelligence Era
		16.2.1 Databases Annotated with Biological Activity
		16.2.2 Opportunities for AI in Ligand‐Based Drug Design
		16.2.3 Opportunities in Structure‐Based Drug Design
	16.3 Chemical Space and Chemical Multiverse
		16.3.1 Recent Progress on Chemical Space
		16.3.2 Chemical Multiverse and Constellation Plots
	16.4 Hit Identification, Optimization, and Development of Bioactive Compounds
		16.4.1 Virtual Screening
		16.4.2 VISAS: General Approach that Expands Bioactive Molecules
			16.4.2.1 ViSAS on an Antituberculosis Chemical Dataset
		16.4.3 De Novo Design Libraries
			16.4.3.1 Case Study: DNMT‐Focused Libraries
	16.5 Extended Similarity Methods
		16.5.1 The Extended Similarity Framework
		16.5.2 Global Description: Chemical Diversity, Chemical Library Networks, and Clustering
		16.5.3 Local Description: Diversity Selection and Medoid Calculation
	16.6 Conclusion and Outlook
	Acknowledgments
	References
ch17
	17.1 Introduction
	17.2 The Origins of SAR Knowledge Bases
	17.3 SAR Knowledge Base Landscape
		17.3.1 Open‐Source SAR Databases
			17.3.1.1 PubChem
			17.3.1.2 ChEMBL
			17.3.1.3 DrugBank
			17.3.1.4 BindingDB
			17.3.1.5 Other Known Open‐Source Databases
		17.3.2 Commercial SAR Databases
			17.3.2.1 GOSTAR
			17.3.2.2 Reaxys Medicinal Chemistry
			17.3.2.3 Other Known Commercial Databases
	17.4 Comparison and Complementarity of SAR Databases
	17.5 Applications of SAR Knowledge Base in Modern Drug Discovery
	17.6 Future Direction
	Acknowledgment
	Disclaimer
	References
ch18
	18.1 Introduction
	18.2 The Cambridge Structural Database (CSD) and CSD‐Based Tools
	18.3 How CSD and CSD Knowledge‐Based Tools Can Aid Drug Discovery
		18.3.1 Target Identification and Target Validation
		18.3.2 Hit Identification
	18.4 Hit‐to‐Lead
		18.4.1 Lead Optimization
	18.5 Challenges in Drug Development
		18.5.1 How CSD Can Further Impact Drug Discovery
	References
ch19
	Chapter 19 Structure‐Based Ultra‐Large Virtual Screenings
		19.1 Introduction
		19.2 Fundamentals
			19.2.1 Receptor Structures and Preparation
			19.2.2 Ligand Preparation and Ligand Libraries
			19.2.3 Molecular Docking
			19.2.4 Virtual Screenings
		19.3 Ultra‐Large Ligand Libraries
			19.3.1 Commercial Libraries
				19.3.1.1 REAL Database and REAL Space
				19.3.1.2 CHEMriya
				19.3.1.3 GalaXi
				19.3.1.4 eXplore
				19.3.1.5 Freedom Space
				19.3.1.6 ZINC Libraries
				19.3.1.7 VirtualFlow Libraries
			19.3.2 Public Virtual Libraries
				19.3.2.1 Generated Databases (GDBs)
				19.3.2.2 KnowledgeSpace
		19.4 Docking‐Based Ultra‐Large Virtual Screenings
			19.4.1 Success Stories
				19.4.1.1 Gorgulla (2018)
				19.4.1.2 Lyu et al. (2019)
				19.4.1.3 Stein et al. (2020)
				19.4.1.4 Gorgulla et al. (2020)
				19.4.1.5 Alon et al. (2021)
				19.4.1.6 Other Success Stories
			19.4.2 Available Software for ULVSs
				19.4.2.1 UCSF DOCK
				19.4.2.2 VirtualFlow
		19.5 Synthon‐Based Virtual Screenings
		19.6 Machine Learning‐Based Virtual Screenings
			19.6.1 Deep Docking
			19.6.2 MolPAL
		19.7 Other Acceleration Techniques
			19.7.1 Deep Learning Approaches to Molecular Docking
			19.7.2 GPU Acceleration of Molecular Dockings
		19.8 Quality of Ultra‐Large Virtual Screening Results
		19.9 Conclusion and Outlook
		References
ch20
	20.1 Introduction
		20.1.1 Overview of Molecular Docking
	20.2 Need for Benchmarking
		20.2.1 Benchmarking Datasets
			20.2.1.1 Benchmarking Sets for Pose Prediction
			20.2.1.2 Benchmarking Sets for Virtual Screening
		20.2.2 Evaluation Metrics
			20.2.2.1 Enrichment Factor
			20.2.2.2 Docking Enrichment (DE)
			20.2.2.3 BEDROC Score
			20.2.2.4 Receiver Operating Characteristic Curve
			20.2.2.5 Root Mean Square Deviation (RMSD)
			20.2.2.6 Real‐Space R Values
			20.2.2.7 Root Mean Squared Error (RMSE)
			20.2.2.8 Spearman\'s Rank‐Order Correlation
			20.2.2.9 Kendall Rank Correlation
	20.3 Community Benchmarking Exercises
		20.3.1 Statistical Assessment of Proteins and Ligands (SAMPL) Challenge
		20.3.2 Drug Design Data Resource (D3R) Challenges
		20.3.3 Critical Assessment of Computational Hit‐Finding Experiments
		20.3.4 Continuous Evaluation of Ligand Protein Predictions (CELPP)
	20.4 Lessons Learned from the Benchmarking Exercises
		20.4.1 Quality of Crystal Structures
		20.4.2 Need of Sufficient Metrics to Assess Docking and Scoring
		20.4.3 Usefulness of Statistics, Error Bars, and Confidence Intervals (CI)
		20.4.4 Requirement of Good Data Set
	20.5 Summary
	References
ch21
	Chapter 21 Advances in the Application of In Silico ADMET Models – An Industry Perspective
		21.1 Introduction
		21.2 QSAR Models
			21.2.1 Conventional QSAR Model Development
				21.2.1.1 Data Curation
				21.2.1.2 Chemical Descriptors
				21.2.1.3 Algorithms
				21.2.1.4 Model Validation and Evaluation Metrics
				21.2.1.5 Utilizing Relevant Properties to Improve QSAR Model Performance
				21.2.1.6 Applicability Domain and Reliability of Individual Prediction
				21.2.1.7 Model Interpretability vs. Predictivity
				21.2.1.8 Model Deployment and Accessibility
			21.2.2 In Silico ADMET Models and Their Influence During Drug Discovery
				21.2.2.1 In Silico ADME Models
				21.2.2.2 In Silico Toxicity Models
				21.2.2.3 Typical QSAR Models for ADMET in the Industry
			21.2.3 Emerging QSAR Technologies and Algorithms
		21.3 Extended Scope of In Silico ADMET
			21.3.1 Exploring New Chemical Space With Generative Models Considering ADMET
			21.3.2 Mechanism‐Based Models
			21.3.3 Predictive MetID (Metabolites Identification From Chemical Structures)
		21.4 Conclusion
		References
ch22
	Chapter 22 Modeling the Structures of Ternary Complexes Mediated by Molecular Glues
		22.1 Introduction
		22.2 Methodology
		22.3 Results and Discussion
			22.3.1 Approach 1: Treating MGs as Whole, Indivisible Molecules
			22.3.2 Approach 2: Treating MGs as “linkerless PROTACs”
		22.4 Conclusions
		References
ch23
	23.1 Introduction
	23.2 Mechanism of Covalent Inhibition
	23.3 Computational Characterization of Reversible Covalent Binding
	23.4 Computational Characterization of Irreversible Covalent Binding
		23.4.1 Computation of the Dissociation Constant of the Noncovalent Step
		23.4.2 Computation of the Rate Constant of the Covalent Step
	23.5 Case Studies of Reversible Covalent Inhibition
	23.6 Case Studies of Irreversible Covalent Inhibition
	23.7 Summary
	References
ch24
	Chapter 24 Orion® A Cloud‐Native Molecular Design Platform
		24.1 Introduction
		24.2 The Platform
		24.3 Target Preparation and Structural Data Organization
		24.4 Virtual Screening
		24.5 Predicting Small‐Molecule Binding Affinity
		24.6 ADMET Prediction and Permeability in Drug Discovery
		24.7 Predicting Drug Crystal Forms
		24.8 Summary
		References
ch25
	25.1 Introduction
	25.2 Complex Molecular Mechanics at Scale
	25.3 Modeling Billions of Molecules in a Day
	25.4 Faster Free Energy
	25.5 Vision for the Future
	25.6 Concluding Remarks
	Disclaimer
	References
ch26
	26.1 What to Expect
		26.1.1 Motivation
		26.1.2 Structure of the Chapter
	26.2 Another New Paradigm
		26.2.1 Digital
		26.2.2 Quantum
			26.2.2.1 Refresher – Quantum Mechanics and Its Features
			26.2.2.2 |WTFUQC⟩ – The ℏ(|y⟩ + |o⟩)pe
		26.2.3 Challenges
			26.2.3.1 Cognitive Challenge – Quantum Literacy
			26.2.3.2 Cultural Challenge
	26.3 Quantum Computing Overview
		26.3.1 Quantum Simulators
		26.3.2 Embedding Quantum into Computing
		26.3.3 What\'s the New Idea?
		26.3.4 Introducing the Concept of the Qubit
			26.3.4.1 Superposition
			26.3.4.2 The Bloch Sphere
			26.3.4.3 Interference
			26.3.4.4 Nondeterminism
			26.3.4.5 Entanglement
			26.3.4.6 Multi‐particle Registers
		26.3.5 Quantum Computing Stack
		26.3.6 Major Applications of Quantum Computing
			26.3.6.1 Classes of Applications
			26.3.6.2 Famous Quantum Algorithms (Gate)
		26.3.7 Gate Quantum Computing
			26.3.7.1 Example: The Hadamard Gate
			26.3.7.2 Example: The CNOT Gate
			26.3.7.3 Development Kits
		26.3.8 Adiabatic/Annealing
			26.3.8.1 Intuitive Explanation
			26.3.8.2 A Brief Explanation
			26.3.8.3 In Summary
			26.3.8.4 Digital Annealer
		26.3.9 Hardware
		26.3.10 Technological Challenges
			26.3.10.1 Errors
			26.3.10.2 Scalability
			26.3.10.3 Conncetivity
		26.3.11 Quantum Computer for Molecular Biology
			26.3.11.1 Local Quantum Effects in Biochemical Processes
			26.3.11.2 Global Quantum Effects in Functional Bio‐Molecules
			26.3.11.3 Quantum Computing for Structural Biology
			26.3.11.4 Quantum Computing for Data‐Driven Approaches to Molecular Biology
	26.4 Quantum Machine Learning
		26.4.1 Introduction
		26.4.2 The Shortcoming of Classical ML Models
		26.4.3 Types of QML
			26.4.3.1 CQ Algorithm Types
			26.4.3.2 Quantum Regression Model or Quantum Algorithm for the Method of Least Squares
			26.4.3.3 Quantum Clustering Model
			26.4.3.4 Quantum Kernel Model or Quantum Support Vector Machines
			26.4.3.5 Quantum Neural Networks
			26.4.3.6 Quantum Generative Models
		26.4.4 Limitations of QML
	26.5 Designing Peptides and Proteins
		26.5.1 Background
		26.5.2 De Novo Rational Design
		26.5.3 Peptide and Protein Design
		26.5.4 Protein Design as an Optimization Problem
		26.5.5 Quantum Optimization for Peptide and Protein Design
		26.5.6 Quantum Annealing Approach
		26.5.7 The qPacker Algorithm
		26.5.8 Gate‐Based Approaches
	26.6 Conclusion
	26.7 Further Reading
	References
index