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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Computational Methods for Rational Drug Design
- •Contents
- •1.1.2.2 GROMACS
- •1.1.2.3 Amber
- •1.1.2.4 CHARMM
- •1.1.2.5 AutoDock
- •1.1.2.6 VMD
- •1.1.2.7 PyMOL
- •1.1.2.8 Open Babel
- •List of Contributors
- •Preface
- •1. Molecular Modeling and Drug Design
- •1.1 Introduction
- •1.1.1 What Is Molecular Modeling?
- •1.1.2 Software Used for Molecular Modeling
- •1.1.2.1 Schrodinger
- •1.1.2.9 Avogadro
- •1.1.2.10 Discovery Studio
- •1.1.3 Molecular Mechanics
- •1.1.3.1 Prediction of Binding Affinity
- •1.1.3.2 Conformational Analysis
- •1.1.3.3 Virtual Screening
- •1.1.3.4 Lead Discovery
- •1.1.3.5 Mechanism of Action
- •1.2 Types of Molecular Models
- •1.2.1 Ball-and-Spoke Model
- •1.2.1.1 Future Directions
- •1.2.2 Space-filling Models
- •1.2.2.1 Future Directions
- •1.2.3 Crystal Lattice Models
- •1.2.3.1 Future Directions
- •1.3 Computational Methods in Drug Discovery
- •1.3.1 What Is Drug Discovery?
- •1.3.2 Computational Platforms for Drug Discovery
- •1.3.2.1 NCBI
- •1.3.2.2 Chemical Databases
- •1.3.2.3 PDB
- •1.3.2.5 UniProt
- •1.3.2.6 QSAR
- •1.3.2.8 Desmond
- •1.3.2.9 OpenBabel
- •1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
- •1.3.2.11 SBML
- •1.3.2.12 Virtual Screening
- •1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
- •1.4 Potential Use and Application of AI in Drug Designing
- •1.4.1 Target Identification and Validation
- •1.4.2 Drug Screening and Lead Optimization
- •1.4.3 De Novo Drug Design
- •1.4.4 Predictive Toxicology and ADMET
- •1.4.5 Clinical Trial Optimization
- •1.4.6 Drug Repurposing
- •1.4.7 Concept of Personalized Medicine
- •1.4.8 Drug Combination Optimization
- •1.5 Limitations of Current Methods
- •1.5.1 Data Restrictions
- •1.5.2 Interpretability
- •1.5.3 Generalization
- •1.5.4 Resources and Computation
- •1.5.5 Ethical Considerations
- •1.5.6 Validation and Experimentation
- •1.5.7 Regulatory Obstacles
- •1.6 Case Studies
- •1.7 Molecular Docking
- •1.7.1 What Is Molecular Docking?
- •1.7.1.1 Procedure
- •1.7.1.2 Biophysical Laws
- •1.7.1.3 Rigid and Flexible Docking
- •1.7.1.4 Types of Docking
- •1.7.1.5 Challenges and Future Perspectives
- •1.7.2 Applications of Molecular Docking in Drug Designing
- •1.7.3 Success of Molecular Docking Cases in Drug Designing
- •1.8 Conclusion and Future Works
- •References
- •2. Bioactive Small Molecules and Drug Discovery
- •2.1 Introduction
- •2.1.1 Introduction to Drug Design and Discovery
- •2.1.2 Brief History of Small-Molecule Drug Discovery
- •2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
- •2.2.1 Structure-Based Methods
- •2.2.2 Ligand-Based Methods
- •2.2.3 Network-Based Methods
- •2.3 Natural Products in Bioactive Small-Molecule Discovery
- •2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
- •2.3.2 Anticancer Agents as Bioactive Molecules
- •2.3.3 Antiviral Agents as Bioactive Molecules
- •2.3.4 Antimalarial Agents as Bioactive Molecules
- •2.6.6 Toxicity and Side Effects
- •2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
- •2.6.8 Structural Diversity and Novelty
- •2.6.9 Patentability and Intellectual Property
- •2.3.5 Marine Bioactive Products
- •2.4.1 Importance of DFT in Small-Molecule Drug Discovery
- •2.5 Application of DFT to Bioactive Small Molecules
- •2.5.1 HOMO–LUMO Calculation
- •2.5.1.1 Molecular Electrostatic Potential (MEP) Map
- •2.5.1.3 Natural Bond Orbital (NBO) Analysis
- •2.5.1.4 Implementations and Tools
- •2.6.1 Target Identification and Validation
- •2.6.2 Target Specificity
- •2.6.3 Bioavailability and Pharmacokinetics
- •2.6.4 Chemical Structure and Drug-likeness
- •2.6.5 Safety and Toxicity
- •2.7 Conclusion
- •References
- •3. Novel Drug Targets for Small Molecule-based Drug Discovery
- •3.1 Introduction
- •3.2 Drug Target Identification
- •3.3 Classification of Novel Drug Targets
- •3.3.1 Transcription Factors
- •3.3.2 Cytokines
- •3.3.3 Chaperones
- •3.3.4 Viral Targets
- •3.3.5 G Protein-coupled Receptors
- •3.3.6 Transporters
- •3.3.7 Enzymes
- •3.3.8 RNA Targets
- •3.4 Small Molecules as Drugs
- •3.5 Conclusion
- •References
- •4.1 Introduction
- •4.2 Structure-Based Drug Discovery Concept
- •4.2.1 Structure Generation of the Target
- •4.2.1.1 The Detailed Description of Each Tool
- •4.2.2 Active Binding Site Within the Target
- •4.2.2.1 The Detailed Description of Each Tool
- •4.2.2.2 Molecular Docking Analysis
- •4.2.2.3 The Detailed Description of Each Tool
- •4.2.3 Molecular Dynamic Simulations
- •4.2.3.1 The Detailed Description of Each Tool
- •4.3 Ligand-Based Drug Discovery Concept
- •4.3.1.1 The Detailed Description of Each Tool
- •4.4 Structure- and Ligand-Based Assisted Studies
- •4.4.1 The Detailed Description of Each Tool
- •4.4.2 The Detailed Description of Each Tool
- •4.5 Advancement and Challenges in SBDD and LBDD
- •4.6 Conclusion
- •References
- •5. Virtual Screening and Lead Discovery
- •5.1 Introduction to Virtual Screening and Lead Discovery
- •5.1.1 Overview of Drug Discovery Process
- •5.1.2 Role of Virtual Screening
- •5.1.3 Importance of Lead Discovery
- •5.2 Molecular Targets and Biomolecular Structures
- •5.3 Virtual Screening Approaches
- •5.3.1 Structure-based Virtual Screening
- •5.3.2 Ligand-based Virtual Screening
- •5.3.3 Hybrid Approaches
- •5.4 Databases and Compound Collections
- •5.4.1 Overview of Chemical Databases
- •5.4.2 Compound Filtering and Preparation
- •5.4.3 Diversity and Size of Compound Collections
- •5.5 Molecular Docking
- •5.5.1 Principles of Molecular Docking
- •5.5.2 Docking Algorithms and Scoring Functions
- •5.5.3 Validation of Docking Results
- •5.6 Pharmacophore Modeling
- •5.6.1 Concept of Pharmacophores
- •5.6.2 Generating Pharmacophore Models
- •5.6.3 Applications in Lead Discovery
- •5.7 Quantitative Structure–Activity Relationship (QSAR)
- •5.7.1 Basics of QSAR
- •5.7.2 Model Development and Validation
- •5.7.3 QSAR in Virtual Screening
- •5.8 Machine Learning and AI in Virtual Screening
- •5.8.1 Introduction to Machine Learning and AI
- •5.8.2 Feature Selection and Model Training
- •5.8.3 Applications in Virtual Screening
- •5.9 Hit-to-Lead Optimization
- •5.9.1 Prioritizing Hits from Virtual Screening
- •5.9.2 SAR Analysis and Iterative Design
- •5.9.2.1 SAR Analysis (Structure–Activity Relationship)
- •5.9.2.2 Iterative Design
- •5.9.3 ADME/Tox Considerations
- •5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
- •5.9.3.2 Toxicity Considerations
- •5.10 Case Studies and Examples
- •5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
- •5.11 Challenges and Future Directions
- •5.11.1 Limitations of Virtual Screening
- •5.11.2 Emerging Technologies and Trends
- •5.11.3 Integration with High-throughput Experimentation
- •5.12 Ethical and Regulatory Considerations
- •5.12.1 Intellectual Property and Patents
- •5.12.2 Ethical Use of Computational Tools
- •5.12.3 Regulatory Approval Process
- •5.13 Conclusion
- •5.13.1 Future Prospects in Virtual Screening and Lead Discovery
- •5.13.2 Summary of Key Points
- •References
- •6. ADMET and Physicochemical Assessments in Drug Design
- •6.1 ADMET
- •6.1.1 Absorption
- •6.1.1.1 Solubility and Dissolution
- •6.1.1.2 Lipophilicity
- •6.1.1.3 Permeability
- •6.1.2 Distribution
- •6.1.3 Metabolism
- •6.1.4 Excretion
- •6.1.5 Toxicity
- •6.2 Physicochemical Assessments
- •6.2.1 Partition Coefficient
- •6.2.2 Log D: Ionizable Compound Lipophilicity
- •6.2.2.1 Methods for Calculating Lipophilicity
- •6.2.2.2 Direct Experimental Determination of Lipophilicity
- •6.2.2.3 Indirect Experimental Determination of Lipophilicity
- •6.2.3 Acid–Base Properties and Ionization
- •6.2.4 Solubility
- •6.2.5 Polymorphism
- •6.2.6 Molecular Weight
- •6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA)
- •References
- •7. In Silico Modeling and Drug Design
- •7.1 Introduction
- •7.2 Target Identification
- •7.2.1 Experimental Approaches
- •7.2.2 Computational Target Identification
- •7.2.3 Target Validation
- •7.3 Computer-Aided Drug Design
- •7.3.1 Ligand-based CADD
- •7.3.2 Structure-Based CADD
- •7.4 ADMET Assessment
- •7.5 Conclusion
- •References
- •8. Pharmacophore Modeling in Drug Design
- •8.1 Introduction
- •8.1.1 The Role of Pharmacophore Modeling in Drug Design
- •8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
- •8.2 Essential Concepts in Pharmacophore Hypothesis Generation
- •8.2.1.1 Partitioning Initial Data into Distinctive Datasets
- •8.3 Diverse Approaches to Pharmacophore Modeling
- •8.3.1 Ligand-Based Pharmacophore Modeling
- •8.3.2 Structure-Based Pharmacophore Modeling
- •8.4 Application of Pharmacophore Modeling
- •8.4.1 Applications of Pharmacophore-Based Virtual Screening
- •8.4.1.1 Drug Discovery
- •8.4.2 Applications in Drug Target Fishing
- •8.4.3 Applications in Ligand Profiling
- •8.4.4 Applications in Docking
- •8.4.5 Applications in ADMET
- •8.4.6 Modulation of the Immune System
- •8.5 Emerging Trends in Pharmacophore Model Development
- •8.5.1 Involvement of Machine Learning
- •8.5.2 Prediction of Pharmacokinetic Properties
- •8.5.3 Structural Biology and Protein Functionality Studies
- •8.5.4 Integration with MDs Simulations
- •8.6 Case Studies
- •8.6.1 Case 1
- •8.6.2 Case 2
- •8.7 Challenges in Pharmacophore Modeling
- •8.8 Conclusion
- •Acknowledgments
- •References
- •9. Scaffold Hopping and De Novo Drug Design
- •9.1 Introduction
- •9.2 Scaffold Hopping
- •9.2.1 Classification of Scaffold Hopping
- •9.2.1.1 1° Hop: Heterocycle Replacement
- •9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
- •9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
- •9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
- •9.2.2 Advantages of Scaffold Hopping
- •9.2.3 Disadvantages of Scaffold Hopping
- •9.2.4 Reasons for Scaffold Hopping
- •9.2.5 Properties and Key Methods of Scaffold Hopping
- •9.3 De Novo Drug Design
- •9.3.1 Classification of De Novo Drug Design
- •9.3.1.1 Structure-based Drug Design
- •9.3.1.2 Ligand-based Drug Design
- •9.3.1.3 De Novo Design Strategies
- •9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
- •9.3.1.5 Hybrid Approaches
- •9.3.2 Basic Principle of De Novo Drug Design
- •9.3.3 Application of De Novo Drug Design
- •9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
- •9.3.5 Methodological Approaches in De Novo Drug Design
- •9.3.5.1 Structure-based De Novo Drug Design
- •9.3.5.2 Ligand-based De Novo Drug Design
- •9.3.5.3 Generation of Drug-Like Molecular Fragments
- •9.3.5.4 Similarity Searching
- •9.3.5.5 Selection of Target Reference Structure
- •9.3.5.6 Similarity Analysis of De Novo-generated Compounds
- •9.3.5.7 Evaluation of Scaffold Diversity
- •9.4 Results and Discussion
- •9.4.1 Generation of Drug-Like Molecular Fragments
- •9.4.2 De Novo Design with a Single Reference Structure
- •9.4.3 De Novo Design with a Focused Set of Five Similar Templates
- •9.4.4 De Novo Design with a Diverse Set of Five Templates
- •9.6 Case Study
- •9.6.1 De Novo Drug Design
- •9.6.2 Scaffold Hopping
- •9.7 Conclusion
- •References
- •10. Fragment-based Drug Design and Drug Discovery
- •10.1 Introduction
- •10.2 The Process of Finding Fragments
- •10.3 FBDD Strategies
- •10.4 Case Studies
- •10.5 Conclusion and Future Perspectives
- •References
- •11. AI/ML Approaches in Drug Design
- •11.1 Introduction
- •11.2 Traditional Drug Design Methods
- •11.2.1 The Rise of Computational Methods
- •11.2.2 The Importance of AI/ML in Modern Drug Design
- •11.3 AI/ML Landscape in Drug Design
- •11.3.1 AI/ML Algorithms and Methods
- •11.3.1.1 Machine Learning Models
- •11.3.1.2 Neural Networks
- •11.3.2 Applications in Drug Design
- •11.3.2.1 Peptide Synthesis
- •11.3.2.2 Molecular Design
- •11.3.2.3 Virtual Screening (VS)
- •11.3.2.4 Quantitative Structure–Activity Relationship Models
- •11.3.2.5 Drug Repurposing
- •11.3.3 Challenges and Failures
- •11.4 Ethics, Reliability, and Regulatory Issues
- •11.5 Future Directions
- •11.6 Conclusion
- •References
- •12. Network-based Methods in Drug Discovery
- •12.1 Introduction
- •12.1.1 Background of Drug Discovery Future Challenges
- •12.1.2 Single Target Approach Limitations
- •12.1.3 Emergence of Network Biology and Polypharmacology
- •12.2 Network Pharmacology: Practical Guide
- •12.2.1 Common Network Pharmacology Databases
- •12.2.1.1 Network Pharmacology-Related Databases and Data Analysis Tools
- •12.2.1.2 Exploring IMPPAT Network Pharmacology Databases
- •12.2.1.3 Target Genes of Phytoconstituents
- •12.2.2 Network Analysis and Visualization
- •12.2.3 Applications of Network Pharmacology in Drug Discovery
- •12.3 Ayurveda and Traditional Indian Medicine
- •12.3.1 Overview of Ayurveda and Its Complex Formulations
- •12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
- •12.4 Network Pharmacology in Herbal Remedies
- •12.4.1 Application of Network Pharmacology in Herbal Drug Discovery
- •12.4.1.1 Cancer
- •12.4.1.2 Cardiovascular Diseases (CVDs)
- •12.4.1.3 Diabetes Mellitus (DM)
- •12.4.2 Screening Pharmacological Efficacy of Herbal Remedies
- •12.4.3 Utilizing Network Pharmacology to Understand Complex Diseases
- •12.5 Conclusion and Future Prospects
- •References
- •13. Rational Design of Natural Products for Drug Discovery
- •13.1 Introduction
- •13.2 Natural Products for the Development of New Drugs
- •13.3 Criteria for Selecting Natural Products for Drug Design
- •13.4 Importance of Biodiversity in Sourcing Natural Products
- •13.5 Structural Elucidation of Natural Products
- •13.6.3 High-Throughput Screening Methods for Efficient Compound Selection
- •13.6.4 Molecular Dynamics Simulations for Predicting Solubility and Stability
- •13.6.5 ADMET Attributes Predicted In Silico
- •13.7 Formulation Challenges with Natural Products
- •13.8 Quality by Design (QbD) Approaches
- •13.8.1 Use of Computational Models for Formulation Optimization
- •13.9 Conclusion
- •References
- •14. Design of Enzyme Inhibitors in Drug Discovery
- •14.1 Introduction
- •14.3 Classification of Enzyme Inhibitors
- •14.3.1 Reversible Inhibitors
- •14.3.2 Irreversible Inhibitors
- •14.3.3 Competitive Inhibitors
- •14.3.4 Noncompetitive Inhibitors
- •14.3.5 Allosteric Modulators
- •14.4.1 Structure-Based Design
- •14.4.2 Computer-Aided Design
- •14.4.3 Fragment-Based Design
- •14.4.4 Virtual Screening Method
- •14.4.4.1 Ligand Based
- •14.4.4.2 Receptor Based
- •14.4.5 Natural Product-Based Discovery
- •14.4.6 Using Iterative Protein Crystallographic Analysis
- •14.4.7 Utilization of Covalent Inhibitors
- •14.4.8 Encapsulation Techniques
- •14.4.9 Based on Active-Site Specificity
- •14.4.10 Machine Learning Inhibitor Design
- •14.4.11 Enzyme-Templated Dynamic Combinatorial Chemistry
- •14.5 Limitations and Challenges
- •14.6 Future Directions
- •14.7 Conclusion
- •References
- •15.1 Introduction
- •15.2 Peptides as Therapeutics
- •15.2.1 Peptide Antibiotics
- •15.2.1.1 Peptides in Bone Diseases
- •15.2.1.2 Peptides in Cancer
- •15.2.1.3 Peptides in Metabolic Diseases
- •15.2.1.4 Peptides in Gastrointestinal Diseases
- •15.2.2 Advantages and Limitations of Peptide Therapeutics
- •15.2.3 FDA-Approved Peptide Therapeutics
- •15.2.4 Peptide-Based Entities in Clinical Trials
- •15.2.5 Peptide Synthesis and Diversification
- •15.2.5.1 Chemical Synthesis of Peptides
- •15.2.5.2 Chemical Modification of Peptide and Peptidomimetics
- •15.2.5.3 Backbone Modification of Peptides
- •15.2.5.4 Side-Chain Modification of Peptides
- •15.2.5.5 Peptide Cyclization
- •15.2.5.6 Peptide Mimicking of α-Helices and Stabilization
- •15.2.5.7 Peptide Mimicking of β-Strands and β-Sheets
- •15.2.5.8 Peptide Production by Recombinant Technology
- •15.2.5.9 Peptides Modification by Genetic Code Expansion
- •15.2.5.10 PEGylation of Peptides and Proteins
- •15.3 New Technologies for Peptide-Based Drug Discovery
- •15.3.1 Phage Display
- •15.3.2 mRNA Display
- •15.3.3 DNA-Encoded Libraries
- •15.3.4 Cell-Penetrating Peptides
- •15.3.5 Macrocyclic Peptides
- •15.4 Computational Approaches in Peptide Drug Discovery
- •15.5 Conclusion
- •References
- •16. Rational Design of Drugs for Neurodegenerative Disorders
- •16.1 Introduction
- •16.2 Common Mechanism of Neurodegeneration
- •16.3 Brief Overview of Computational Methods in Drug Design
- •16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder
- •16.4.1 Epidemiology of Parkinson’s Disease
- •16.4.2 Pathogenesis of PD
- •1) Accumulation of Lewy bodies in substantia nigra
- •2) Mitochondrial dysfunction
- •3) Genetic factors
- •4) Neuroinflammation
- •5) Impaired protein handling
- •6) Oxidative stress
- •7) Environmental toxins
- •16.4.3 Signaling Pathway of Parkinson’s Disease
- •1) DA signaling
- •2) MAPK/ERK pathway
- •3) PI3K/Akt/mTOR pathway
- •4) Wnt/β-catenin pathway
- •5) NF-κB (nuclear factor-κB) pathway
- •6) Autophagy-lysosomal pathway
- •7) JNK (c-Jun N-terminal kinase) pathway
- •8) AMPK (AMP-activated protein kinase) pathway
- •9) Nrf2 (nuclear factor erythroid 2-related factor 2) pathway
- •16.4.4 Enzymatic Targets in Parkinson’s Disease
- •1) MAO-B (monoamine oxidase B)
- •2) COMT (catechol-O-methyltransferase)
- •3) LRRK2
- •4) GCase (glucocerebrosidase)
- •5) PARP-1 [poly(ADP-ribose) polymerase-1]
- •6) PINK1
- •7) DJ-1 (Parkinson protein 7)
- •8) Nrf2
- •16.4.5 Current Therapeutic Approaches to Treat PD
- •1) Drugs to treat motor symptoms of PD
- •2) Drugs to treat non-motor symptoms of PD
- •3) Disease-modifying therapies to treat PD
- •16.4.6 Current Therapeutic Challenges to Treat Parkinson’s disease
- •1) Symptomatic relief only
- •2) Motor fluctuations and dyskinesias
- •3) Limited efficacy in nonmotor symptoms
- •4) Disease progression
- •5) Side effects
- •6) Limited treatment options for advanced PD
- •7) Individual variability
- •16.4.7 Unmet Needs in Parkinson’s Disease Therapeutics
- •16.4.8 Significance of Computational Approaches in Parkinson’s Disease
- •16.4.9 Use of Computational Tools in Identifying Biomarkers
- •16.4.10 Neuroprotective Strategies Through Computational Insights
- •16.4.10.1 Computational Models for Neuroprotection
- •1) Target identification and validation
- •2) Drug repurposing
- •3) Alpha-synuclein aggregation inhibitors
- •4) Deep learning in biomarker discovery
- •5) Personalized medicine
- •6) Drug-induced neuroprotection
- •7) Optimizing clinical trials
- •1) ML and AI-based diagnostics
- •2) Wearable technology integration
- •3) Multimodal data fusion
- •4) Predictive modeling of disease progression
- •5) Network analysis of brain connectivity
- •6) Personalized treatment optimization
- •7) Data sharing and collaboration platforms
- •16.5 Conclusion
- •References
- •17. Rational Design of Anti-inflammatory Therapeutics
- •17.1 Introduction
- •17.2 Navigating Inflammation and its Microenvironment
- •17.2.1 Inflammatory Cell Infiltration and Vascular Permeability
- •17.2.2 Acidosis
- •17.2.3 Increased Oxidative Stress in Tissues
- •17.3 The Demand for Advanced Anti-inflammatory Medications
- •17.5 Rational Design of Anti-inflammatory Agents
- •17.5.2 New Anti-inflammatory Agent with Indoyl-imidazole Hybrids
- •17.5.3 Rational Design of Novel Aminopiperidinyl Amide
- •17.5.4 Lipid Nanoparticles (LNPs) as Anti-inflammatory Agents
- •17.6 Conclusion and Future Perspectives
- •Authors’ Contribution
- •References
- •18.1 Introduction
- •18.2 Treatment
- •18.3 Antibacterial Resistance
- •18.3.1 Mutation
- •18.3.2 Horizontal Gene Transfer (HGT)
- •18.3.3 Enzymatic Modification or Degradation
- •18.3.4 Target Site Modification
- •18.3.5 Decreased Permeability
- •18.3.6 Efflux Pumps
- •18.3.7 Plasmids
- •18.3.8 Transposons
- •18.3.9 Gene Amplification
- •18.3.10 Formation of Biofilms
- •18.3.11 Modified Metabolic Pathways
- •18.3.12 Adaptive Evolution
- •18.4.1 Structure- Based Drug Design
- •18.4.2 Modification of Existing Antibiotics
- •18.4.3 Bioisosterism
- •18.4.4 Prodrug Strategies
- •18.4.5 Similar Bacterial Components Target
- •18.4.6 Combine or Combination Therapy
- •18.4.7 Drug Repurposing
- •18.4.8 Resistant Mechanism Blocking
- •18.4.9 Improving Drug Delivery by Nanotechnology
- •18.4.10 Phage Intervention
- •18.4.11 Host Targeting
- •18.4.12 CRISPR-Cas Technique
- •18.4.13 Peptides as Antibacterials
- •18.4.14 Immunizations and Immunotherapy
- •18.4.15 Natural Product Derivatives
- •18.4.16 Fragment- Based Drug Discovery (FBDD)
- •18.4.17 Metabolomics and Genetics
- •18.4.18 Cheminformatics
- •18.5 Summary and Conclusion
- •References
- •19. Rational Design of Antiviral Therapeutics
- •19.1 Introduction to Antiviral Therapeutics
- •19.1.1 Overview
- •19.1.2 Blueprints for Antiviral Drug Interventions
- •19.1.2.1 Protein Folding and Binding Sites
- •19.1.2.2 Conformational Changes
- •19.1.2.3 Protein–Protein Interactions (PPIs)
- •19.1.2.4 Capsid and Envelope Structures
- •19.1.2.5 Structural Vulnerabilities
- •19.1.2.6 Enzymatic Activities
- •19.1.2.7 Viral Attachment
- •19.1.2.8 Viral Assembly and Replication Machinery
- •19.1.2.9 The Host’s Immune Response
- •19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
- •19.2.1 Enzyme Inhibitors
- •19.2.2 Antiviral Peptides
- •19.2.3 Antiviral Antibodies
- •19.2.4 Lipid-Mimicking Compounds
- •19.2.5 Vaccines
- •19.2.6 Immunomodulation
- •19.3 Rational Strategies for Antiviral Therapeutics
- •19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
- •19.3.2 AI and ML
- •19.3.3 Systems Biology and Network Pharmacology
- •19.3.4 CRISPR Systems
- •19.3.5 Nanotechnology-Based Design and Delivery Systems
- •19.3.6 Reverse Vaccinology
- •19.4 Conclusion
- •References
- •20. Rational Design of Anticancer Therapeutics
- •20.1 Introduction
- •20.2 Rational Design of Nanomedicine for Cancer Treatment
- •20.4.1 Particle Size
- •20.4.2 Shape
- •20.4.3 Surface Modification
- •20.6 Artificial Intelligence’s Progress in Anticancer Drug Development
- •20.6.1 Identification of Anticancer Drug Targets Using Artificial Intelligence
- •20.6.3 Artificial Intelligence-Based De Novo Anticancer Drug Design
- •20.6.4 Artificial Intelligence for Repurposing Anticancer Drugs
- •20.7 Conclusion
- •References
- •21. PROTAC and ProTide Strategies in Drug Design
- •21.1 Introduction
- •21.2 Drug Design: Past to Present
- •21.3 PROTAC Strategy in Drug Design
- •21.3.1 Ubiquitin Proteasome System and PROTACs
- •21.3.2 Chemical Formulations of PROTACs
- •21.3.3 Advent of PROTACs as Antiviral
- •21.3.4 NS3/4A-Targeting PROTACs Against HCV
- •21.3.4.1 Neuraminidase-Targeting PROTACs
- •21.4 Emergence of ProTide Technology in Drug Design
- •21.5 Approaches of ProTides in Drug Development
- •21.6 Implementation of ProTides as Nucleoside Analogs
- •21.6.1 Antiviral Applications of ProTides
- •21.7 Conclusion
- •References

423
19.1 Introduction to Antiviral Therapeutics
19.1.1 Overview
Viral infections are a significant category of diseases caused by viruses, which are tiny, infectious
agents composed of genetic material (either DNA or RNA) surrounded by a protein coat [1].
Viruses do not possess cellular structures and do not have metabolic processes, which make them
intracellular parasites, unlike bacteria. The world of antiviral therapeutics is closely connected to
the building blocks of life we know as biomolecules. These majorly include carbohydrates, lipids,
nucleic acids, and proteins, which play a crucial role in our defense against viruses [2]. An efficient
way to fight viruses is by designing novel target-specific bioactive compounds that interact with
these biomolecules to alter their function in favoring human health. The major focus for designing
these compounds is on DNA and RNA [1, 2], as these nucleic acids are like instruction manuals for
the cells, including the ones that viruses use. By changing how these instructions are read, it is
possible to disrupt the viral processes, inhibiting their activity and probably eradicating them from
the host cell [3]. Similarly, proteins that replicate viruses can also be targeted by designing com-
pounds that inhibit the viral spread. Moreover, Pillay et al. [4] have summarized key points for
drug resistance, approximately 25 years later drug resistance remains a major challenge for
researchers and clinicians across the globe [4, 5].
However, the rational design of bioactive agents for antiviral activity represents a critical aspect in
the development of effective antiviral drugs and treatments. The approach leverages the prior
knowledge of the viral target and its mechanism to computationally screen and develop
multigenerational drug-combating viruses [5, 6]. Advances in various scientific disciplines,
including organic physical chemistry, theoretical chemistry, computational chemistry, and
pharmacochemistry, have significantly contributed to the implementation of sophisticated
algorithms aimed at predicting the interactions between organic ligands and their biological
receptors [5]. Computer-aided drug design (CADD) and virtual screening (VS) have gained
prominence in designing antivirals. These computational approaches, particularly molecular
docking techniques employed by software like Autodock and Rosetta Ligand (details are mentioned
19
Rational Design of Antiviral Therapeutics
Sneha Dokhale
1
, Samiksha Garse
2
, Shine Devarajan
2
, Vaishnavi Thakur
2
,
and Shaunak Kolhapure
2
1
Department of Biotechnology, B. K. Birla College of Arts, Science & Commerce, Kalyan, Maharashtra, India
2
School of Biotechnology and Bioinformatics, D Y Patil Deemed to be University, Navi Mumbai, Maharashtra, India

19 Rational Design of Antiviral Therapeutics424
in Section 19.3.1 [6], play a fundamental role in identifying optimal binding modes of candidate
molecules within the binding cavities of viral targets [6]. These methods enable researchers to assess
the potential efficacy of various compounds and prioritize them with their favorable binding
interactions. The bioinformatics tools empower researchers in the rational design of novel antiviral
agents based on the three-dimensional structure of drug targets. In addition, it allows for the
creation of molecular structures with enhanced affinity and specificity toward viral targets,
optimizing the potential antiviral activity of the designed molecules. Structural biology information
provides an invaluable understanding of the precise interactions between bioactive molecules and
viral targets by resolving X-ray crystallography structures of target–ligand complexes [7]. These
structural insights prove essential for the development of potent antiviral drugs. Moreover, the
integration of CADD with artificial intelligence, machine learning, and systems biology (SB)
approaches has proven fruitful in the development of antiviral therapeutics with a personalized touch.
19.1.2 Blueprints for Antiviral Drug Interventions
Structural informatics is the foundation of antiviral research, providing a glimpse into the intricate
architecture and dynamics of viral biomolecules. Through advanced techniques like X-ray
crystallography, cryo-electron microscopy (cryo-EM), and nuclear magnetic resonance (NMR)
spectroscopy, structural biology reveals the atomic complexity of these biomolecules [8]. This data
is instrumental in identifying precise viral targets for antiviral drug development. It unveils the
spatial arrangement of viral proteins, nucleic acids, and lipids, shedding light on their active sites
and binding pockets [9]. Researchers can then rationally design bioactive molecules to precisely
interact with these viral targets. This enables the creation of antiviral compounds that disrupt
essential viral functions with exceptional specificity and continuously optimize bioactive molecules
to enhance their potency and reduce side effects [10]. The data also aids in predicting potential
drug resistance mutations by highlighting regions of viral biomolecules that are less amenable to
binding [11]. This guides the development of drugs resistant to viral mutation. Ultimately, the
utilization of structural data in antiviral research is paramount, as it empowers scientists in the
pursuit of effective antiviral therapeutics, bringing us one step closer to combating viral infections
on a global scale [4, 5]. Figure 19.1 illustrates the structural blueprint with a generic description of
each component’s structural function with its inhibition actions on viral assembly.
Apart from these, there are some additional factors that are taken into consideration for drug
intervention as follows.
19.1.2.1 Protein Folding and Binding Sites
Viral proteins have unique three-dimensional structures that are crucial for their functions. These
structures are determined by the protein sequence and are often stabilized by hydrogen bonds,
disulfide bridges, and hydrophobic interactions [12]. Understanding protein folding is important
because it provides researchers with insights into potential drug-binding sites and regions vulner-
able to disruption [7, 13]. Moreover, many antiviral drugs work by binding to specific sites on viral
proteins. These sites can be active sites where chemical reactions occur or allosteric sites that mod-
ulate protein function [13, 14]. In some cases, drugs mimic the natural substrates of viral enzymes
and compete for binding, inhibiting the enzymatic activity [13].
19.1.2.2 Conformational Changes
Enveloped viruses often rely on conformational changes within the fusion proteins that are located
on their envelopes. These changes fuse the viral envelope with the host’s membrane, wherein the
genetic material reaches out into the host’s cytoplasm [15]. Many viruses have protective protein

19.1 Introduction to Antiviral Therapeutics 425
coats or capsids surrounding their genetic material. During the assembly of new viral particles,
conformational changes in viral structural proteins are required to facilitate the packaging of the
viral genetic material into newly formed capsids [16]. This step is crucial for the production of
infectious virions. The alterations of surface proteins pose as a viral strategy to escape recognition
by antibodies or immune cells, contributing to their ability to persist in the host [17]. Similarly,
these changes play a major role in viral enzymes (e.g., polymerases) [18]; they enable the replica-
tion of viral genetic material or transcribe it into mRNA [19].
Some antiviral drugs can bring about conformational change in the structure of viral proteins. The
changes induced by antiviral drugs, in the virus family Picornaviridae, more specifically
poliovirus [20] underline mechanisms that involve the interactions of the ligand-induced
conformational drugs and the viral capsid proteins. Crystal structures of drug complexes, particularly
with the compounds R80633 and R77975 [21], have been studied to assess the structural alterations
and understand their impact on viral infection [22]. The unique structure of the poliovirus capsid,
with its characteristic pocket and surface features, makes it susceptible to interactions with specific
antiviral drugs. The drugs either slightly exceed or fall short of the optimal size for the binding
pocket. This results in imperfect packing at both ends of the pocket, influencing the interaction
between capsid proteins [21, 23]. The recognized amino acids (Met1132, Leu1134, and Leu1261) in
the drug-binding pocket are critical for determining the efficiency of drug binding [23]. The
mechanical responses and structural shifts observed near the fivefold axis provide insights into how
the poliovirus family responds to antiviral drug-induced stress. These antiviral compounds, which
prevent viral uncoating by impeding conformational changes required for entering the host, have
been scrutinized in complex with the Mahoney strain of type 1 poliovirus [21, 24]. Notably, the
Mahoney strain displays relative insensitivity toward the antivirals, demanding substantial spatial
changes to allow drug binding. These alterations not only affect the nearby binding site of the drug
but also farther off the loops that lie within the viral particle’s fivefold axis. The shifts correlated with
drug effectiveness provide insights into how antiviral drugs interfere with the normal conformational
repertoire of the virus, ultimately inhibiting productive cell entry [24, 25].
Protects genetic material and facilitate its entry
into host cells
Inhibition: hinders the assembly of new
infectious virions, limiting virus infection
Capsid
Directs the synthesis of viral
components, including proteins
and
genetic material
Inhibition: blocking polymerase, reverse
transcription, protease, integration, entry,
using RNA interference, and targeting
helicases
Genetic
material
Facilitates viral attachment to host cells
Binding
receptors
Inhibition: block the binding of viral proteins to host
cell receptors, inhibiting the initial step of infection
Facilitating attachment, entry into host
cells, and evasion of the immune system
Inhibition: use of fusion inhibitors or
entry blockers can prevent viral entry
Viral envelope
proteins
Facilitates attachment and entry into host
cells by containing glycoproteins that
recognize receptors, enabling fusion, or
endocytosis
Lipid bilayer
envelope
Inhibition: blocking fusion and
attachment to prevent infection
Enzymes
Molecular knockout
Essential for viral replication and
maturation
Inhibition: target viral enzymes, limiting the
virus’s ability to replicate and propagate within
host cells
Figure 19.1 Molecular knockouts of viruses.

19 Rational Design of Antiviral Therapeutics426
19.1.2.3 Protein–Protein Interactions (PPIs)
Viral proteins interact with host proteins, enabling the various stages of the viral life cycle.
Drugs can be designed to disrupt these interactions by targeting specific structural elements
involved in binding. Interfering with PPIs can block critical steps in viral replication and
assembly [26].
19.1.2.4 Capsid and Envelope Structures
Capsids and envelopes are essential for viral entry into the host cell, assembly, and protection of
viral genetic material [22]. Understanding the structural components of capsids and envelopes is
vital for designing drugs that target these elements. For example, drugs that can interfere with the
formation or stability of viral capsids can prevent successful viral replication [15, 27].
19.1.2.5 Structural Vulnerabilities
Some viral components, irrespective of the virus type, exhibit structural vulnerabilities that could
serve as targets for drug intervention [28]. These vulnerabilities might include flexible loops,
exposed hydrophobic regions, or critical enzymatic sites essential for viral replication or entry
into host cells. For example, in addition to the fusion peptide (FP) domain [29] in the SARS-
CoV-2 spike (S) protein, other viruses possess analogous regions or domains susceptible to
inhibition.
In general, these structural vulnerabilities represent potential sites for small-molecule
intervention. By targeting these regions with specific inhibitors, such as chlorcyclizine (CCZ) [28,
30] or other compounds, it becomes feasible to disrupt viral replication, entry, or other essential
processes crucial for viral survival. Importantly, these vulnerabilities often differ between viruses
or even among strains of the same virus, offering a unique opportunity for the development of
selective drug therapies tailored to combat specific viral infections [28]. HIV-1 Env (envelope
protein), similar to other viral surface glycoproteins, exhibits a higher degree of varied sequence
and glycosylation that facilitates evade strategies against host immune responses. However, recent
advances in structural characterization have revealed vulnerable sites on the HIV-1 envelope,
responsible for receptor binding and fusion machinery [16]. These vulnerabilities have been
targeted broadly by neutralizing antibodies (nAbs) [31], leading to the development of potential
strategies for vaccine design or the creation of antiviral therapeutics.
19.1.2.6 Enzymatic Activities
Viral enzymes are essential components of the intricate machinery employed by viruses to infect
host cells, replicate their genetic material, and evade host defenses. Among these enzymes, viral
proteases, polymerases, and deubiquitinases (DUBs) play pivotal roles in viral pathogenesis [32].
Viral proteases, exemplified by the M48USP, a protease found in cytomegalovirus (CMV) [33],
exhibit unique catalytic triads enabling them to cleave specific host and viral proteins, further
facilitating viral replication and evasion of host immune responses [32, 33]. Similarly, DUBs like
BPLF1 in Epstein–Barr virus (EBV) and UL36USP in human cytomegalovirus (HCMV) exert a
multifunctional role in viral replication and immune evasion by modulating the ubiquitination
status of proteins and interfering with host innate immune pathways [32]. Structural studies of
these viral enzymes have provided valuable insights into their catalytic mechanisms and substrate
specificities, facilitating the development of antiviral drugs targeting their activities [12]. Small-
molecule inhibitors and engineered viral mutants deficient in enzyme activity represent promising
strategies for combating viral infections by disrupting essential enzymatic functions crucial for
viral replication and immune evasion [23, 34].

19.2 Targets for Antiviral Therapeutics and Inhibition Strategies 427
19.1.2.7 Viral Attachment
Many viruses attach to host cell receptors through specific interactions between viral proteins and
receptor molecules. Enveloped viruses often employ fusion proteins to facilitate the merger of viral
and host cell membranes. Membrane fusion is a crucial step in the life cycle of enveloped viruses
involving key structural elements and mechanisms [15]. Enveloped viruses possess a lipid bilayer
membrane derived from the host cell, with embedded viral envelope proteins, that include fusion
proteins undergoing conformational changes typically containing hydrophobic fusion peptides or
domains [35, 36]. Once the fusion process begins, the attachment of viral envelope proteins to host
cell receptors triggers conformational changes exposing FPs. These peptides insert into the host cell
membrane, forming fusion intermediates and transient fusion pores [36]. The viral and host cell
membranes merge, as the fusion pores expand, allowing viral content release into the host cell [35].
Understanding these structural elements and mechanisms is crucial for designing antiviral strate-
gies that target specific steps in the fusion process, potentially preventing viral entry and infection.
19.1.2.8 Viral Assembly and Replication Machinery
Viral assembly and release of the genetic components represent key stages in the viral lifecycle,
where new virions are formed and then released from host cells. These processes are highly
orchestrated and offer valuable targets for antiviral therapeutics. The replication machinery is a
critical cellular process, which is responsible for the accurate duplication of DNA or RNA during
cell division [1]. In situations like viral infections, inhibiting this machinery becomes essential to
prevent uncontrolled viral replication. Biomolecules offer promising avenues for developing
antiviral therapeutics that target the replication machinery [18, 27].
19.1.2.9 The Host’s Immune Response
The host immune response is a complex and highly regulated system that plays a critical role in
defending the body against infections and diseases. It involves various components, including
white blood cells, antibodies, and cytokines, working together to recognize and eliminate pathogens
like bacteria, viruses, and fungi [17]. While the immune system is essential for maintaining health,
some pathogens have evolved mechanisms to evade or interfere with the host’s immune response.
This includes mechanisms like antigenic variation, where pathogens change their surface proteins
to avoid recognition, and where they can mimic host molecules to avoid immune attack [31].
Suppression of immune activation: Some pathogens produce bioactive molecules that actively
suppress the host’s immune response. For example, certain viruses can inhibit the production of
interferons, which are essential signaling molecules for antiviral defense. Researchers are exploring
strategies to block or counteract these immune-suppressing bioactive molecules, allowing the
immune system to mount a more effective response [37, 38].
HCMV has evolved several proteins that directly target key components of the host’s immune sys-
tem and often mimic or inhibit their immune responses. For example, the HCMV protein pUL83, also
known as pp65, can interfere with antigen presentation by inhibiting the transporter associated with
antigen processing (TAP), thereby reducing the presentation of viral antigens to cytotoxic T cells [39].
19.2 Targets for Antiviral Therapeutics and Inhibition Strategies
Bioactive molecules employ sophisticated strategies to inhibit viral entry by involving receptor
blockade compounds that mimic host cell receptors or bind directly to viral attachment proteins,
preventing initial virus–cell interactions. Fusion inhibition targets conformational changes in

19 Rational Design of Antiviral Therapeutics428
viral fusion proteins postreceptor binding, halting membrane fusion, and viral entry [27, 36].
Coreceptor interference involves blocking coreceptor binding sites on viral proteins and impeding
their effective interaction with primary receptors and coreceptors. By targeting viral receptors,
such as CD4, CXCR4, and CCR5 for HIV [16, 34, 40], or salicylic acid analogs for influenza [27],
promising avenues for antiviral intervention are revealed. The discovery of novel receptors,
exemplified by chANXA2 for ALV-J, underscores the potential of host-targeted approaches in
combating viral infections, with identified host receptors serving as valuable markers for diverse
antiviral strategies. pH modulation comes into play with viruses entering via endocytosis, by
raising endosomal pH, this strategy disrupts fusion events essential for cytoplasmic entry [41].
Allosteric inhibition affects distant structural sites on viral proteins, inducing conformational
changes that impair viral entry [7, 13, 14]. Preventing membrane fusion targets viral fusion
proteins or interferes with fusion pore formation, effectively blocking virus-cell membrane
merging. Intracellular signaling interference disrupts host cell signaling pathways used by some
viruses for entry. RNA interference (RNAi) can specifically target and degrade viral RNA
sequences, inhibiting viral replication and entry [42].
19.2.1 Enzyme Inhibitors
For catalyzing transcription and replication, viruses often use their own RNA and DNA
polymerase that reflects diversity in the replication machinery [18]. Studying the crystal
structures of viral polymerases provides insights into their active sites and binding pockets.
Inhibition of viral polymerases can be achieved through competitive binding of small molecules
or biomolecules, disrupting RNA and DNA synthesis. Helicases [43] and nucleoproteins [44]
play pivotal roles in unwinding viral RNA or DNA, coating and protecting viral genomes. In
measles and Ebola viruses, helicase is absent where nucleoprotein functions as helicase [45]. In
Herpes Simplex virus type 1 (HSV-1), a helicase-primase complex poses as a potential alternative
for the development of antiviral drugs [46]; for example, Amenamevir (AMNV) disrupts the
complex and halts viral replication [47]. Viral proteases encode proteases for processing viral
polyproteins and are essential for the maturation of viral particles [48]. Structural insights
provide the basis for rationally developing compounds that fit snugly into the active site, blocking
protease activity, and disrupting the maturation of viral proteins, thereby inhibiting viral
assembly and release [12]. There are enzyme inhibitors targeting reverse transcriptase [49],
which are either nucleoside or nucleotide specific [50]. Table 19.1 discusses some inhibitors
targeting viral pathogens.
19.2.2 Antiviral Peptides
Antiviral peptides are designed based on intricate structural insights into viral proteins involved in
assembly and release [35]. By precisely targeting the interaction sites, antiviral peptides can
obstruct the assembly of viral components or interfere with PPIs, disrupting viral assembly. For
example, the FP of the HIV-1 envelope glycoprotein, structural studies have unveiled its interaction
with host cell membranes during viral entry [35, 36]. Antiviral peptides designed to mimic this FP
can competitively bind to host cell membranes [27], thwarting the viral envelope’s fusion process
with the host cell membrane [36]. This disruption profoundly impedes the final stages of viral
entry, assembly, and release [19]. Enfuvirtide prevents fusion and blocks the formation of new
virions of HIV further hindering the viral infection. Boceprevir and telaprevir are synthesized
peptides that inhibit protease to restrict hepatitis C replication [35, 36].

19.2 Targets for Antiviral Therapeutics and Inhibition Strategies 429
Table 19.1 Target inhibitors of viral structure.
Inhibitors Type Drug name Effects on target disease Reference
Attachment
inhibitors
Fostemsavir Reduces HIV-1 growth by blocking a
virus’s ability to bind with its CD4
receptor
[34]
Neuraminidase
inhibitors
Oseltamivir
(Tamiflu)
Attaches to and inhibits the
neuraminidase enzymes’ active site,
which is necessary for all influenza
viruses to release their progeny
virions from infected host cells
[51]
Zanamivir By its antagonist action, it inhibits
neuraminidase protein, which also
prevents the virus from spreading
and infecting neighboring cells
[52]
Entry inhibitors Fusion inhibitors Enfuvirtide Inhibits various types of
encapsulated viruses, especially
HIV strains
[34]
Maraviroc Blocks the CCR5 coreceptor to
prevent HIV-1 from entering
host cells
[34]
Protease
inhibitors
Nelfinavir Inhibits cytochrome P450 3A
and HIV protease
[53]
Darunavir It maintains to have antiretroviral
efficacy against certain HIV strains
that are resistant to multiple drugs
[54]
Polymerase
inhibitors
DNA polymerase
inhibitors
Valacyclovir It is used to treat cytomegaloviruses,
varicella-zoster, and herpes
[55]
RNA polymerase
inhibitors
Ribavirin By attaching to the substrate-binding
site of the IMPDH enzyme and
preventing it from accessing its
endogenous substrate, Ribavirin
inhibits the enzyme, lowering GTP
levels and production
[56]
Nucleoside
polymerase
inhibitors
Sofosbuvir It effectively inhibits the SARS
Virus-CoV-2 RdRp
[53]
Nonnucleoside
polymerase
inhibitors
Rilpivirine HCV-NS5B polymerase inhibitor of
hepatitis C virus (HCV)
[57]
Nucleoside and
nucleotide
reverse
transcriptase
inhibitors
Abacavir Abacavir’s intracellular anabolite
carbovir-triphosphate inhibits HIV
viral RNA-dependent DNA
polymerase (reverse transcriptase),
which inhibits viral replication and
has an antiviral effect
[49]
Tenofovir Prevent HIV replication by posing a
rival to deoxyadenosine
5′-triphosphate, which is naturally
occurring and the substrate used by
HIV transcription, for DNA insertion
[50]
(Continued)

19 Rational Design of Antiviral Therapeutics430
19.2.3 Antiviral Antibodies
Monoclonal antibodies (mAbs) represent a potent class of biomolecules designed to recognize and
bind to specific viral proteins, particularly those involved in assembly and release [31]. Structural
biology provides critical insights into epitopes on viral proteins that are susceptible to antibody
binding. Detailed structural studies show the conformational changes and PPIs central to viral
assembly and release, enabling the selection or design of mAbs that can precisely target these
epitopes, thereby interfering with the viral lifecycle [60, 61]. Studies on SARS-CoV-2 have unveiled
the structure of its spike protein, which plays a pivotal role in viral entry, assembly, and release [29].
mAbs are engineered to bind specific regions of the spike protein that can disrupt viral attachment,
assembly, and release by blocking interactions with host cell receptors, thus offering an avenue for
antiviral intervention [31]. mAbs like Bamlanivimab, Etesevimab, and Casirivimab pose as a pas-
sive immunotherapy for mild to moderate infection that acts as ACE2 blockers inhibiting viral
entry [31].
19.2.4 Lipid-Mimicking Compounds
The design of compounds that mimic the lipid composition of host cell membranes depends on an
understanding of the structural properties of lipid bilayers and the interactions that occur between
the viral envelope proteins and lipids [62]. This understanding guides the development of molecules
that can effectively compete with host cell lipids, thereby disrupting viral envelope formation and
impeding viral budding [36]. Some lipid-mimicking compounds imitate the lipid rafts present in
host cell membranes. Because of this, there is a disruption in the assembly of viral envelope
proteins into lipid rafts, preventing the proper formation of viral envelopes [42]. This interference
halts the release of mature virions and significantly reduces viral infectivity [22].
Table 19.1 (Continued)
Inhibitors Type Drug name Effects on target disease Reference
Nonnucleoside
reverse-
transcriptase
inhibitors
Efavirenz It inhibits the HIV reverse
transcription enzyme’s activity by
attaching to a noncatalytic location
on the enzyme. As a result, this
activity prevents HIV replication and
other DNA polymerase-related
activities
[16]
Etravirine It causes direct inhibition of the
human immunodeficiency virus type
1 (HIV-1) reverse transcriptase
enzyme. It limits the action of
polymerase that is dependent on both
DNA and RNA by directly binding to
reverse transcriptase
[58]
Integrase
inhibitors
Raltegravir Prevents HIV-1 DNA from being
inserted into the genome of the host
cell by inhibiting the action of
HIV-1 integrase
[40]
Dolutegravir Inhibits integrase binding and blocks
the HIV DNA transfer into the host
[59]

19.3 Rational Strategies for Antiviral Therapeutics 431
19.2.5 Vaccines
Another aspect of antiviral therapeutics is vaccines that mimic the natural immunity against the
virus. Antigens, adjuvants, or genetic materials like RNA or DNA (vectors) are commonly used in
these types of vaccinations to activate and identify the viral proteins and trigger the defense
processes [42]. Antigens are like key players that imitate the viral protein and produce
immunological responses through the synergistic effect of creating antibodies (by B cells) and
enhancing immunity (by T cells) [39, 44]. Adjuvants can stimulate the production of cytokines and
other immune mediators, leading to a stronger and longer-lasting immune response. For example,
the oral polio vaccine (OPV) and the measles, mumps, and rubella (MMR) vaccine [63], to reduce
its virulence and prevent it from infecting healthy individuals with disease, the live pathogen is
modified. Bioactive molecule-based immunizations use specific molecules (mRNA, viral vectors,
or purified proteins) that encode or imitate a pathogen component [64]. When provided, they
stimulate cells to produce pathogenic components, which sets off an immune reaction without
causing sickness. Vector development process can be viral (harmless viruses) and nonviral
(nanoparticles, liposomes, etc.) that carry and deliver genes to the host cells that decode for
antigens to enhance immune recognition [42]. The human papillomavirus (HPV) vaccine
(Gardasil and Cervarix) neutralizes antibody responses thereby averting HPV infection and
reducing the chances of cervical cancer [65]. Some of the nanoparticle-based vectors are discussed
in Section 19.3.5.
19.2.6 Immunomodulation
Bioactive molecules can also be used to modulate the immune response in cases of autoimmune
diseases or chronic inflammation. Immunomodulatory therapies aim to balance the immune
system, either by suppressing an overactive response or by enhancing a weakened one [39, 44].
These strategies often involve the use of bioactive compounds like mAbs or cytokine inhibitors [31].
Looking at the example of HCMV employs a range of clever tactics to manipulate the human immune
system, particularly through its viral cytokine-like molecules [39]. These molecules, such as cmvIL-10
and LAcmvIL-10, are key players in altering the body’s immune responses. CmvIL-10 acts as an immune
suppressor by decreasing the activity of MHC-II, which is an essential component for recognizing and
responding to infections [66]. Simultaneously, it enhances the immune system’s ability to identify and
eliminate infected cells by boosting the expression of receptors that aid in recognizing and engulfing
these cells. In contrast, LAcmvIL-10’s role involves hiding HCMV from immune cells, particularly
CD4+ cells, which are vital for detecting and combating infections. By making it more challenging for
CD4+ cells to identify HCMV-infected cells, LAcmvIL-10 aids the virus in evading immune surveillance
and establishing long-term persistence within the host. These modulation strategies highlight HCMV’s
adaptability in manipulating the immune system to its advantage [39, 66].
19.3 Rational Strategies for Antiviral Therapeutics
With the revolutionalized technologies and integration of Omics, rational drug designing strategies
have overpowered the traditional and serendipitous discoveries, allowing researchers to overcome
drug resistance challenges and understand the complexity of host–pathogen interaction.
Figure 19.2 illustrates the multidisciplinary integrated pipeline for the development of antiviral
therapeutics including CADD, artificial Intelligence (AI) and machine learning (ML) algorithms,
and SB. Antiviral therapeutics based on these approaches are mentioned in Table 19.2.

19 Rational Design of Antiviral Therapeutics432
19.3.1 CADD and QSAR (Quantitative Structure–Activity Relationship)
CADD is based on either target- or drug-oriented pipelines, which are also known as structure- and
ligand-based approaches, respectively. In the structure-based (target-oriented) approach, the
structural data of the target is known; these could be viral enzymes or structural proteins of interest.
In the case of an unknown structure, protein modeling allows structure prediction based on the
templates that are experimentally validated [77]. AlphaFold, an AI-generated tool to predict the 3D
structure against the primary structure of protein [14], can be further used to determine the function
of the target. Once this information is availed, docking and molecular dynamics (MD) are performed
to investigate the molecular features like binding affinity and conformational modifications, leading
to probable therapeutics of various drugs [78, 79]. In the ligand-based (drug-oriented) approach, the
3D structure of the target is unknown, but with the prior structural and functional features of the
drugs, the targets can be identified by preparing ligands through VS. Lipinski Rule of 5 approach can
also be used to filter compounds based on basic properties like molecular weight, log P, and the
number of H-bond acceptors and donors [79]. Nowadays with the advances in technology, the CADD
approach integrates AI and ML models for the VS [30, 73]. The pharmacophore-based ligand
searching plays a crucial role in identifying structural key features that mimic and comprehend
biological activity. Pharmacophore docking can also be done if the 3D structure of the target is
available [11, 37]. These features can be ranked according to electrostatic and steric descriptors.
Hence, ligand- and structure-based mapping and searching are very important in selecting
appropriate candidate compounds. Dai et al. identified two promising drugs with the same structural
approach, to inhibit SARS-CoV protease, an enzyme that mediates the replication of virus [29].
Similarly, Ahmed et al. (2024) used homology modeling for building the nonstructural protein (nsP4)
of RdRp of the Chikungunya virus and ProdDrug server for predicting active sites that serve as
druggable pockets. Pharmacophore modeling and VS filtered thousands of compounds with the
highest likelihood for the predicted pockets. Docking and MD result in three ligands with optimal
interactions with three subdomains of the nsP4, i.e., catalytic, thumb, and palm triggering specific
inhibition responses against the virus [11].
Target-based
approach
Structure-
based
approach
Study complex biological
interactions, targeting
multiple components for
optimized therapeutics
Enhancing
bioavailability and
minimizing side
effects
Analyze viral genomes,
predict epitopes
computationally, design
synthetic vaccines
1.
Identification
of viral
targets
2.
Hit
identification
and
validation
3.
AI+
ML
integration
4.
System and
network-
based
pharmacology
5.
QSAR
modeling
6.
Nano
drug
delivery
system
7.
Drug
repurposing
8.
Reverse
vaccinology
9.
Personalized
vaccinology
10.
Preclinical
and
clinical
testing
Predicting
drug
interactions
Virtual
screening
Predict and optimize the
biological activity compounds
using computational tools for
enhanced efficacy
Screen and repurpose
FDA-approved drugs
for antiviral activity
Tailor vaccines to individual
genomics and immune
responses, optimizing efficacy
Lead
optimization
Pipeline for anti-viral therapeutics
Figure 19.2 Rational pipeline for the development of antiviral therapeutics.
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