b67d0af1_Cover
Front-Matter_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Molecular Docking for Computer-Aided Drug Design: Fundamentals, Techniques, Resources and Applications
Copyright_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Copyright
Dedication_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Dedication
ac69b085_xv
List-of-Contributors_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
List of Contributors
Preface_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Preface
Acknowledgments_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Acknowledgments
Chapter-1---Modern-Tools-and-Techniques-in_2021_Molecular-Docking-for-Comput
1. Modern Tools and Techniques in Computer-Aided Drug Design
1. Overview of Computer-Aided Drug Design
2. Chemical Libraries
3. Structure-Based Approaches and Screening
3.1 Target Structure and Validation
3.2 Molecular Docking and Virtual Screening
3.2.1 Sampling algorithm
3.2.2 Scoring function
3.2.2.1 Support vector machine
3.2.2.1 Support vector machine
3.2.2.2 Random forest
3.2.2.2 Random forest
3.2.2.3 Artificial neural network
3.2.2.3 Artificial neural network
3.2.2.4 Deep learning
3.2.2.4 Deep learning
3.3 De Novo Drug Design
4. Ligand-Based Approaches and Screening
4.1 Molecular Fingerprint and Similarity Searches
4.2 Pharmacophore Modeling
4.2.1 Water pharmacophore approach
4.2.2 Dynamic pharmacophore approach
4.2.3 Ab initio specificity/selectivity pharmacophore approach
4.3 Quantitative Structure–Activity Relationship
5. Applications of CADD in Drug Discovery
5.1 Virtual Screening
5.2 Lead Optimization
5.3 Scaffold Hopping
5.4 Multitargeted Approaches/Polypharmacology
5.5 Drug Repurposing/Reprofiling
6. Conclusions
References
Chapter-2---Biomolecular-Talks-Part-1--A-Theoreti_2021_Molecular-Docking-for
2. Biomolecular Talks—Part 1: A Theoretical Revisit on Molecular Modeling and Docking Approaches
1. Biomolecules and Their Interactions
1.1 Biomolecules
1.2 Biomolecular Interactions
1.2.1 Types of biomolecular interactions
1.2.1.1 Bonded interactions
1.2.1.1 Bonded interactions
1.2.1.2 Nonbonded interactions
1.2.1.2 Nonbonded interactions
1.3 Need for Computational Approaches
2. Computational Approaches to Study Biomolecular Interactions
2.1 Molecular Mechanics–Based Approaches
2.1.1 Bonded interaction energies
2.1.2 Nonbonded interaction energies
2.1.3 Solvation models
2.1.3.1 Explicit solvent models
2.1.3.1 Explicit solvent models
2.1.3.2 Implicit solvent models
2.1.3.2 Implicit solvent models
2.1.4 Binding energy
2.2 Molecular Dynamics–Based Approaches
2.3 Applications of Molecular Mechanics–Based Force Fields in Drug Design
3. Molecular Docking
3.1 Conformational Landscape Search
3.2 Types of Docking
3.2.1 Rigid docking
3.2.2 Semiflexible docking
3.2.2.1 Systematic search techniques
3.2.2.1 Systematic search techniques
3.2.2.2 Stochastic search techniques
3.2.2.2 Stochastic search techniques
3.2.3 Flexible docking
3.2.3.1 Single protein conformation
3.2.3.1 Single protein conformation
3.2.3.2 Multiple protein conformations
3.2.3.2 Multiple protein conformations
3.3 Scoring Function and Types
3.3.1 Force field–based scoring functions
3.3.2 Empirical scoring functions
3.3.3 Knowledge-based scoring functions
3.3.4 Machine learning–based scoring functions
4. Conclusion
References
Chapter-3---Post-processing-of-Docking-Res_2021_Molecular-Docking-for-Comput
3. Post-processing of Docking Results: Tools and Strategies
1. Introduction
2. Scoring Functions
3. Representation of Ligand–receptor Complexes
3.1 Interaction Fingerprints
3.2 Ligand-based Approaches
3.3 Interaction Fingerprint Databases
4. Post-processing Strategies
4.1 Visual Inspection
4.2 Automatic Post-processing Protocols
4.2.1 Consensus scoring
4.2.2 Artificial intelligence in the serve of docking poses rescoring
4.2.3 Multistep ML-based protocols for docking results evaluation
5. Conclusions
References
Chapter-4---Best-Practices-for-Docking-B_2021_Molecular-Docking-for-Computer
4. Best Practices for Docking-Based Virtual Screening
1. Introduction
2. Docking-Based Virtual Screening
3. Best Practices in Molecular Docking for Virtual Screening
3.1 Calculation of Protonation States and Structural Waters
3.2 Pose Prediction
3.3 Assessing the Performance of DBVS
3.4 Post-Docking Processing
3.4.1 Binding free energy estimations
3.4.2 Score normalization
3.4.3 Consensus scoring
3.4.4 Machine learning–based scoring functions
4. Incorporating Protein Flexibility
4.1 Soft Docking Approach
4.2 Side Chain and Backbone Flexibility Approaches
4.3 Average Protein Grid Approaches
4.4 Ensemble Docking Approach
5. Molecular Docking in Fragment-Based Drug Discovery
6. Case Studies of DBVS
7. Concluding Remarks and Future Directions
References
Chapter-5---Virtual-Libraries-for-Docking-Method_2021_Molecular-Docking-for-
5. Virtual Libraries for Docking Methods: Guidelines for the Selection and the Preparation
1. Introduction
2. Data Collection Within the Vast Chemical Space
2.1 Actual Compounds Collections
2.1.1 Commercial and academic databases
2.1.2 Bioactivity databases
2.1.3 Natural products
2.2 Virtual Compounds Collections
2.2.1 Fragment-based databases
2.2.2 Tangible compounds
2.2.3 Possible compounds
2.2.4 Virtual compounds and virtual screenings
2.3 Library Selection and Customization
3. Database Preparation
3.1 Database Cleaning
3.1.1 Standardization
3.1.1.1 Data curation
3.1.1.1 Data curation
3.1.1.2 Structure normalization (protonation, ionization, tautomerization, and stereo-chemistry)
3.1.1.2 Structure normalization (protonation, ionization, tautomerization, and stereo-chemistry)
3.1.2 Removal of duplicates
3.1.3 Generation of 3D conformers
3.2 Database Filtering
3.2.1 ADME (absorption, distribution, metabolism, elimination) filtering
3.2.2 Toxicity filtering
3.2.3 PAINS (pan-assay interference compounds) alerts
3.3 Automated Tools for Virtual Screening Databases Preparation
4. Conclusion
List of Abbreviations
References
Chapter-6---3D-Structural-Determination-of-Macr_2021_Molecular-Docking-for-C
6. 3D Structural Determination of Macromolecules Using X-ray Crystallography Methods
1. Introduction
2. Protein Purification
3. Protein Crystallization
3.1 Crystallization Process
3.2 Nucleation
3.3 Mechanics of Nucleation
3.3.1 Homogeneous nucleation
3.3.2 Heterogeneous nucleation
3.4 Crystal Growth
3.5 Crystallization Methods
3.6 Crystal System
4. X-ray, Synchrotron, and XFELS
4.1 X-ray
4.2 Synchrotron Radiation
4.3 X-ray Free Electron Lasers
5. Data Collection and Processing
5.1 Protein Crystal Selection and Mounting
5.2 Data Collection Strategies
5.3 Data Processing
6. Solving Phases in MR, SIR/MIR, and SAD/MAD Methods
6.1 Phase Problem
6.2 Molecular Replacement Method
6.3 Issues of Molecular Replacement Method
6.4 Multiple Isomorphous Replacement
6.5 Single-Wavelength Anomalous Diffraction
6.6 MultiWavelength Anomalous Dispersion
7. Structure Solution
7.1 Heavy Atom Analysis and Phase Determination
7.2 Scaling Between Native and Derivative Datasets
7.3 Isomorphous Difference Patterson Map
7.4 Patterson Method
7.5 The Difference Fourier
8. Structure Refinement and Model Building
8.1 Interpretation of Electron Density Maps
8.2 Model Building in Interactive Computer Graphics Environment
8.3 Refinement of the Protein Structure
9. Characterization and Cross-Validation of Solved 3D Structures
9.1 Constraints and Restraints
9.2 Identification of Solvent Sites and Structure Analysis
10. Structure Validation
11. Conclusion
References
Chapter-7---Electron-Microscopy-and-Single-Particl_2021_Molecular-Docking-fo
7. Electron Microscopy and Single Particle Analysis for Solving Three-Dimensional Structures of Macromolecules
1. Introduction
2. The Major Techniques in Structural Biology
2.1 X-Ray Crystallography and Nuclear Magnetic Resonance
2.2 Single Particle Analysis
3. Electron Microscope
3.1 Instrumentation
3.1.1 Electron gun
3.1.2 Electromagnetic lenses
3.1.3 Fluorescent screen/charge coupled detector
3.2 Sample Preparation Methods
3.2.1 Negative staining
3.2.2 Cryo-EM
3.2.3 Cryo-negative staining
3.2.4 Two-dimensional crystallization
3.3 EM Grids
4. Outline of Single Particle Analysis
4.1 Principles of Single Particle Analysis
4.1.1 EM data acquisition and particle selection
4.1.2 Initial model generation
4.1.3 Projections and classification of data set
4.1.4 Class averages and rebuilding the model
4.2 Available Software Tools
5. Remarkable Results From Single Particle Analysis
5.1 Negatively Stained Samples
5.1.1 Understanding the virus encapsulation mechanism
5.1.2 Ligand-induced conformational change
5.1.3 Identification of several conformations of the protein using multimodel refinement
5.1.4 Analyzing the conformational change of a protein in different environments
5.2 Cryo-EM Samples
5.2.1 Novel SARS-CoV-2 virus
5.3 Cryo-Negative Samples
6. Single Particle Analysis and Drug Discovery
7. Conclusions and Future Directions
References
Chapter-8---Computational-Modeling-of-Protein-T_2021_Molecular-Docking-for-C
8. Computational Modeling of Protein Three-Dimensional Structure: Methods and Resources
1. Introduction
2. Protein Structural Organization
3. Sequence–Structure–Function Relationship
4. Experimental Approaches to Determine Structures
5. Computational Approaches for Predicting Protein Structures
5.1 Template-Based Homology Modeling
5.1.1 Selection of best templates
5.1.2 Alignment of template and target sequence
5.1.3 Building model of target protein
5.1.3.1 Generation of the backbone 3D structure
5.1.3.1 Generation of the backbone 3D structure
5.1.3.2 Loop modeling to correct the folding of low-homology regions
5.1.3.2 Loop modeling to correct the folding of low-homology regions
5.1.3.3 Side chain modeling through conformational search
5.1.3.3 Side chain modeling through conformational search
5.1.4 Model optimization
5.1.4.1 Energy minimization
5.1.4.1 Energy minimization
5.1.4.2 Molecular dynamics
5.1.4.2 Molecular dynamics
5.1.5 Model validation
5.2 Template-Based Threading
5.3 Template-Free Modeling (Ab initio)
5.3.1 Conformational search using energy function
5.3.2 Model selection
5.3.3 Reconstruction of all-atom structure
5.3.4 Refinement of structure
6. Case Study and Resources for Modeling Protein Structures
6.1 Protein Sequence Analysis
6.2 Protein Structure Prediction and Evaluation
6.2.1 SWISS-MODEL
6.2.2 MODELLER
6.2.3 I-TASSER
6.2.4 Robetta
6.2.5 Comparison of structure assessment for the predicted models
7. Conclusions and Future Directions
References
Chapter-9---Resources-for-Docking-Base_2021_Molecular-Docking-for-Computer-A
9. Resources for Docking-Based Virtual Screening
1. Drug Discovery
1.1 CADD for Lead Discovery and Development
2. A Brief View of Docking and Its Use
3. Resources for Docking
3.1 Target Information
3.2 Ligand Information
3.3 Docking Tools
3.3.1 Virtual screening tools
3.3.2 Reverse docking tools
3.4 Computational Facilities
3.4.1 Virtual screening in cloud, grid, and distributed computing environments
3.4.2 SARS-CoV-2 research using supercomputing facilities
4. Conclusion
References
Chapter-10---Do-It-Yourself-Dock-It-Yourself--General-_2021_Molecular-Dockin
10. Do It Yourself—Dock It Yourself: General Concepts and Practical Considerations for Beginners to Start Molecular Ligand–Targ ...
1. Introduction
2. Selecting the Docking Program and Associated Tools
3. Installing and Launching ADV, ADT, SPDBV, and Vega ZZ
3.1 Installing the Four Programs on the Computer
3.2 Starting the Four Programs
3.3 Starting ADT and ADV for Newcomers to MS-DOS
4. Creating the Virtual Docking Laboratory
4.1 Creating a Protocol File for the ADT and ADV Start Procedure
4.2 Organizing the Virtual Docking Laboratory on the Computer Desktop
4.3 Using Molecular File Formats in the Virtual Docking Laboratory
4.4 Combining Swiss PDB Viewer and Vega ZZ
5. Preparing the Input Structures of the Target Proteins
5.1 Downloading the 3D Models of the Target Proteins
5.2 Inspecting the Target 3D Models Under SPDBV
5.3 Separating Ligand and Target Molecules Under SPDBV
5.4 Cleaning the 3D Models of Target Proteins
6. Preparing the Small Organic Compound Ligands
6.1 Generating New or Modified 3D Ligand Models Under Vega ZZ
6.2 Optimizing the Geometry of the 3D Models
7. Generating the Search Space and Running the Docking Simulations
8. Analyzing the Graphical Docking Results
9. Analyzing the Numerical Docking Results
10. Synopsis of the Input, Processing, and Output Steps for ADV Docking
11. Solutions for Problems With the Programs (Trouble Shooting)
12. Applicability of the Presented Docking Procedures
13. Conclusion
References
Chapter-11---Use-of-Molecular-Docking-as-a-D_2021_Molecular-Docking-for-Comp
11. Use of Molecular Docking as a Decision-Making Tool in Drug Discovery
1. Introduction
2. Molecular Docking Simulation
2.1 Major Types of Interaction Between Ligand and a Protein (Target)
2.1.1 Electrostatic energy in protein–ligand complexes
2.1.2 Van der Waals potential in protein–ligand complexes
2.1.3 Hydrogen bonds in protein–ligand complexes
2.1.4 Hydrophobic interaction in protein–ligand complexes
2.1.5 Covalent bond formation in protein–ligand complexes
2.2 Different Approaches for Molecular Docking
2.2.1 Rigid and flexible docking models
2.2.2 Fragment-based molecular docking
3. Integrated Computational Methods Involving Molecular Docking
3.1 Combining Molecular Docking With Molecular Dynamics Simulations
3.1.1 Protein–ligand binding and solvent treatment with molecular dynamic simulations
3.1.2 Optimization of ligand conformation by Molecular dynamic simulations
3.2 Combining Molecular Docking and Pharmacophore Models
4. Conclusion and Outlook
References
Chapter-12---Biomolecular-Talks-Part-2--Applicat_2021_Molecular-Docking-for-
12. Biomolecular Talks—Part 2: Applications and Challenges of Molecular Docking Approaches
1. Introduction
2. Applications of Molecular Docking
2.1 Protein–Drug Interactions
2.1.1 Hit identification by virtual screening
2.1.2 Docking integrated with advanced methods
2.1.3 Docking integrated with molecular dynamic simulations and machine learning
2.1.4 Docking in drug repositioning
2.2 Drug–DNA Interactions
2.3 Protein–Nucleic Acid Interactions
2.4 Protein–Protein Interactions
2.5 Protein–Peptide and Small Molecule Interactions
2.6 Protein–Protein Interactions
3. Challenges in Molecular Docking Studies
3.1 Ligand Structures
3.1.1 Optimal three-dimensional geometries
3.1.2 Tautomeric equilibria and protonation states
3.1.3 Formal charges
3.1.4 Physicochemical properties
3.2 Target Structure
3.2.1 Refinement of three-dimensional structure
3.2.2 Protonation states
3.2.3 Presence of metal ions, cofactors, and waters
3.2.4 Selection of binding site
3.2.5 Scoring functions and search algorithms
4. Computational Tools/Servers Available for Docking
5. Conclusion
References
Chapter-13---Application-of-Docking-fo_2021_Molecular-Docking-for-Computer-A
13. Application of Docking for Lead Optimization
1. Introduction
2. Approaches to Lead Optimization
3. Structure-Based Lead Optimization
4. Molecular Docking for Lead Optimization
5. Methods for Calculating Binding Affinities for Lead Optimization
5.1 Free Energy Perturbation
5.2 Linear Interaction Energy
5.3 Molecular Mechanics Poisson–Boltzmann
5.4 Quantum Mechanics
6. Machine Learning–Based Lead Optimization
7. Limitations of Molecular Docking for Lead Optimization
8. Predictive Toxicity Using Docking and Machine Learning
9. Case Studies
9.1 Case Study 1: Syk Inhibitors
9.2 Case Study 2: c-Src/Abl Inhibitor
9.3 Case Study 3: Replication Protein A Inhibitor
9.4 Case Study 4: Neuraminidase Inhibitor
10. Conclusion
References
Chapter-14---Multi-Target-Drugs-as-Master-Keys-to_2021_Molecular-Docking-for
14. Multi-Target Drugs as Master Keys to Complex Diseases: Inverse Docking Strategies and Opportunities
1. Introduction
2. Computational Strategies for Multi-Target Drug Design
3. Case Examples
3.1 Polypharmacology in Drug Attrition and Side Effects
3.2 Polypharmacology in Complex Diseases
3.3 Polypharmacology in Drug Repurposing
4. Concluding Remarks and Future Perspectives
References
Chapter-15---Drug-Repositioning--Principles--Resources_2021_Molecular-Dockin
15. Drug Repositioning: Principles, Resources, and Application of Structure-Based Virtual Screening for the Identification of A ...
1. Introduction
2. Computer-Aided Virtual Screening
2.1 Ligand-Based Virtual Screening
2.2 Structure-Based Virtual Screening
3. Drug Repositioning
3.1 Signature-Based Drug Repurposing
3.2 Network-Based Drug Repurposing
3.3 Literature Mining and Knowledge-Based Drug Repurposing
3.4 Target-Based Drug Repurposing Using Structure-Based Virtual Screening
3.4.1 Basic principles and resources for structure-based virtual screening
3.4.2 Application of structure-based virtual screening for drug repurposing in cancer
4. Conclusion
References
Chapter-16---Design-and-Discovery-of-Kinase_2021_Molecular-Docking-for-Compu
16. Design and Discovery of Kinase Inhibitors Using Docking Studies
1. Introduction
2. Protein Kinases as Drug Targets
3. Structure of Protein Kinases
4. Dynamics of Protein Kinases
5. Types of Inhibitors
6. Molecular Docking in Design and Discovery of Kinase Inhibitors
6.1 Targeting RAF/MEK/ERK Pathway
6.2 EGFR Kinase Inhibitors
6.3 VEGFR Kinase Inhibitors
6.4 JAK Kinase Inhibitors
7. Structure-Guided Strategies for Overcoming Kinase Inhibitors Resistance
8. Molecular Dynamics in Design and Discovery of Kinase Inhibitors
9. Molecular Dynamics Approaches to Enhance Docking
10. Application of Molecular Dynamics to Describe Allostery
11. Machine Learning Approaches to Enhance Docking
12. Conclusion
References
Chapter-17---Docking-Approaches-Used-in-Ep_2021_Molecular-Docking-for-Comput
17. Docking Approaches Used in Epigenetic Drug Investigations
1. Epigenetics
1.1 Writers in Epigenetics
1.2 Reader in Epigenetics
1.3 Erasers in Epigenetics
2. Epigenetics and Diseases
3. Drug Discovery of Epigenetic Modulators with Docking
3.1 DNA Methyltransferases Inhibitors
3.2 Histone Lysine Methyltransferase Inhibitors
3.3 Histone Lysine Demethylase Inhibitors
3.4 Bromodomain Inhibitors
3.5 Histone Acetyltransferase Inhibitors
3.6 Histone Deacetylase Inhibitors
4. Conclusion and Future Perspectives
References
Chapter-18---Molecular-Docking-for-Natural-Produ_2021_Molecular-Docking-for-
18. Molecular Docking for Natural Product Investigations: Pitfalls and Ways to Overcome Them
1. Introduction
2. Peculiarities in Natural Product Investigations
3. Molecular Docking for Natural Product Investigations
3.1 Docking Algorithms and Scoring
3.2 Databases
3.3 Targets and Bioassay Considerations for Successful Docking
3.3.1 Does the biological test system really verify the target?
3.3.2 Target fishing with docking?
3.3.3 Finding the binding site in the target
3.4 Validation of Docking Workflows
3.4.1 Validation by redocking
3.4.2 Validation by cross-docking
3.4.3 Validation by using test and training set data
4. Other Computational Methods for Natural Product Investigations
5. Recent Examples of Docking in Natural Product Research
6. Conclusions
References
Chapter-19---Advances-in-Docking-Based-Drug-De_2021_Molecular-Docking-for-Co
19. Advances in Docking-Based Drug Design for Microbial and Cancer Drug Targets
1. Introduction
2. Structure-Based Drug Design
2.1 Ligands as Inhibitors of Target Protein
2.2 Protein–Protein Interaction Inhibitors
2.3 Application for Covalent Drugs
3. Ligand-Based Virtual Screening
4. In Silico Fragment-Based Drug Design
4.1 Fragment Library Generation
4.2 Virtual Screening of Fragment Libraries
4.3 Optimization of Fragment Hits
4.4 Application of In Silico Fragment-Based Drug Design
5. In Silico Drug Repurposing
6. Reverse or Inverse Drug Designing
7. Antibiotic Resistance and Drug Designing Through Docking
8. Conclusion
References
References
Chapter-20---Role-of-Bioinformatics-in-S_2021_Molecular-Docking-for-Computer
20. Role of Bioinformatics in Subunit Vaccine Design
1. Introduction
2. Basics of Vaccine
3. Properties of an Ideal Vaccine and Its Components
4. Bioinformatics in Vaccine Design
5. Computational Design of Vaccine Constructs
5.1 Antigen Selection
5.2 Epitope Prediction
5.3 Linkers Selection
5.4 Adjuvant
5.5 Design of Vaccine Construct/s
6. Computational Prediction of Physicochemical Properties of Designed Vaccine Construct/s
6.1 Antigenicity Prediction
6.2 Allergenicity Prediction
6.3 Toxicity Prediction
6.4 Solubility and Stability Prediction
7. Structure Prediction, Protein–Protein Interactions, and Complex Stability Analysis
8. An Overview of Successful Vaccine Design Using Bioinformatics
9. Conclusion
References
Chapter-21---Computational-Approaches-Toward-Devel_2021_Molecular-Docking-fo
21. Computational Approaches Toward Development of Topoisomerase I Inhibitor: A Clinically Validated Target
1. Introduction
2. Structure of Human Topoisomerase I
3. Inhibitors of Human Topoisomerase I
4. Computational Approaches for the Discovery of Topoisomerase I Inhibitors
4.1 Quantitative Ligand-Based Pharmacophore Methods
4.2 Structure-Based Drug Design Methods
4.3 Scaffold Hopping Methods
5. Conclusion
References
Chapter-22---Docking-Based-Virtual-Screening-Usi_2021_Molecular-Docking-for-
22. Docking-Based Virtual Screening Using PyRx Tool: Autophagy Target Vps34 as a Case Study
1. Introduction
2. PyRx 0.8 as a Docking-Based Virtual Screening Tool
3. Case Study: DBVS to Identify Small Molecule Inhibitors of Vps34
3.1 Vps34—a Crucial Regulator of the Autophagy Process
3.2 Docking-Based Virtual Screening Using PyRx 0.8
3.2.1 Step 1: Retrieving 3D structure of Vps34
3.2.2 Step 2: Protein structure refinement
3.2.3 Step 3: PyRx installation and basic operations
3.2.4 Step 4: Protein preparation
3.2.5 Step 5: Ligand preparation
3.2.6 Step 6: Receptor grid generation
3.2.7 Step 7: Docking-based virtual screening
3.2.8 Step 8: Result analysis
4. Discussion
5. Conclusion
References
Chapter-23---Molecular-Docking--A-Contempo_2021_Molecular-Docking-for-Comput
23. Molecular Docking: A Contemporary Story About Food Safety
1. Introduction
2. Food Safety
3. Databases and Big Data in Food Safety
4. In silico Methods
4.1 Molecular Docking
5. Case Studies
5.1 Mycotoxins Detection
5.1.1 Aflatoxins and ochratoxins
5.1.2 Zearalenone
5.1.3 Alternariol
5.2 Ellagitannin Metabolites
5.3 Printing Inks
5.4 Food Additives
5.5 Bisphenols in Food
6. Conclusions
References
Index_2021_Molecular-Docking-for-Computer-Aided-Drug-Design
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z
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