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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

xxi
Raghu Ram Achar
Division of Biochemistry
School of Life Sciences
JSS Academy of Higher Education & Research
Mysuru, Karnataka
India
Department of Biotechnology
JSS Science and Technology University
Mysuru, Karnataka
India
Richie Rashmin Bhandare
Department of Pharmaceutical Sciences
College of Pharmacy and Health Sciences
Ajman University, Ajman
United Arab Emirates
Centre of Medical and Bio-allied Health
Sciences Research
Ajman University, Ajman
United Arab Emirates
Abanish Biswas
Department of Pharmaceutical Sciences
and Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Diptanil Biswas
Department of Statistical Genomics
National Institute of Biomedical Genomics
Kalyani, West Bengal
India
Ulviye Acar Çevik
Department of Pharmaceutical Chemistry
Faculty of Pharmacy
Anadolu University, Eskişehir
Turkey
Debarupa Dutta Chakraborty
Royal School of Pharmacy
The Assam Royal Global University
Guwahati, Assam
India
Prithviraj Chakraborty
Royal School of Pharmacy
The Assam Royal Global University
Guwahati, Assam
India
Shrimanti Chakraborty
Department of Pharmaceutical Sciences
and Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Soumi Chakraborty
Department of Pharmaceutical Sciences
and Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
List of Contributors

List of Contributorsxxii
N. Chandana
Division of Biochemistry, School of Life
Sciences
JSS Academy of Higher Education & Research
Mysuru, Karnataka
India
Department of Biotechnology
JSS Science and Technology University
Mysuru, Karnataka
India
Tabsum Chhetri
Department of Bioinformatics
University of North Bengal
Darjeeling, West Bengal
India
Sohan S. Chitlange
Department of Pharmaceutical Chemistry
Dr. D. Y. Patil Institute of Pharmaceutical
Sciences and Research
Pune, Maharashtra
India
Jay Mukesh Chudasama
Department of Pharmacy
Sumandeep Vidyapeeth Deemed to be
University
Vadodara, Gujarat
India
Bhrigu Kumar Das
Pharmacology and Toxicology Laboratory
School of Pharmaceutical Sciences
Girijananda Chowdhury University, Guwahati
India
Riddhi Dave
Gujarat Arts and Science College
Ahmedabad, Gujarat
India
Shine Devarajan
School of Biotechnology and Bioinformatics
D Y Patil Deemed to be University
Navi Mumbai, Maharashtra
India
Sneha Dokhale
Department of Biotechnology
B. K. Birla College of Arts, Science &
Commerce
Kalyan, Maharashtra
India
Samiksha Garse
School of Biotechnology and Bioinformatics
D Y Patil Deemed to be University
Navi Mumbai, Maharashtra
India
John J. Georrge
Department of Bioinformatics
University of North Bengal
Darjeeling, West Bengal
India
Abhirup Ghosh
School of Biosciences and Technology
Vellore Institute of Technology
Vellore, Tamil Nadu
India
Rahul Ghosh
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Ashis Kumar Goswami
Department of Pharmaceutical Sciences
Faculty of Science and Engineering
Dibrugarh University
Assam
India
Shikha Goswami
Department of Pharmacology
Delhi Pharmaceutical Sciences and Research
University
New Delhi
India

List of Contributors xxiii
Anmol Gupta
Department of Biosciences
Integral University, Lucknow
Uttar Pradesh, India
Ayşen Işik
Department of Biochemistry
Faculty of Science, Selçuk University, Konya
Turkey
Vanesa James
Department of Regulatory Affairs and Quality
Assurance
LJ Institute of Pharmacy
Ahmedabad, Gujarat
India
Risy Namratha Jamullamudi
Department of Pharmacy
Koneru Lakshmaiah Education Foundation
Vaddeswaram, AP
India
Priyanka Kamaria
Department of Pharmaceutical Chemistry
KLE College of Pharmacy
Bangalore
India
Koyel Kar
Department of Pharmaceutical Chemistry
BCDA College of Pharmacy and Technology
Kolkata, West Bengal
India
Abdüllatif Karakaya
Department of Pharmaceutical Chemistry
Faculty of Pharmacy, Zonguldak Bulent Ecevit
University, Zonguldak
Turkey
Priya Kashav
Department of Pharmacy
Sumandeep Vidyapeeth Deemed to be
University
Vadodara, Gujarat
India
Ankita Kashyap
Institute of Pharmacy
Assam Medical College and Hospital
Dibrugarh
India
Monalisa Kesh
Centre for Digital Health
Indian Institute of Technology Bombay
Mumbai
India
Kevser Kübra Kırboğa
Faculty of Engineering, Bioengineering
Department
Bilecik Seyh Edebali University
Bilecik
Türkiye
Shaunak Kolhapure
School of Biotechnology and Bioinformatics
D Y Patil Deemed to be University
Navi Mumbai, Maharashtra
India
Sathish Kumar Konidala
Department of Pharmaceutical Sciences
School of Biotechnology and Pharmaceutical
Sciences
Vignan’s Foundation for Science, Technology
and Research
Guntur, AP
India
Nigam Jyoti Maiti
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Manshi Mishra
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India

List of Contributorsxxiv
Saurav Kumar Mishra
Department of Bioinformatics
University of North Bengal
Darjeeling, West Bengal
India
Maitreyee Mukherjee
Department of Pharmaceutical Technology
NSHM Knowledge Campus – Group of
Institutions
Kolkata 700053,West Bengal
India
Podila Naresh
Department of Pharmaceutical Sciences
School of Biotechnology and Pharmaceutical
Sciences
Vignan’s Foundation for Science, Technology
and Research
Guntur, AP
India
Chandan Nayak
School of Pharmaceutical Education and
Research
Berhampur University
Berhampur, Odisha
India
André M. Oliveira
Department of Environment Studies
Federal Centre of Technological Education of
Minas Gerais
Contagem, Minas Gerais
Brazil
Ipsa Padhy
Department of Pharmaceutical Chemistry
School of Pharmaceutical Sciences
Siksha ‘O’ Anusandhan (Deemed to be University)
Bhubaneswar, Odisha
India
Ipsita Panigrahi
Division of Biochemistry
School of Life Sciences
JSS Academy of Higher Education & Research
Mysuru, Karnataka
India
Department of Biotechnology
JSS Science and Technology University
Mysuru, Karnataka
India
Ghanshyam Parmar
Department of Pharmacy
Sumandeep Vidyapeeth (Deemed to be
University)
Vadodara, Gujarat
India
Vaishali Patel
Department of Pharmaceutics
Laxminarayandev College of Pharmacy
Bharuch, Gujarat
India
Mukesh Kumar Patwa
Department of Microbiology
King George Medical University
Lucknow, Uttar Pradesh
India
Sathiaseelan Perumal
Department of Chemistry
Bishop Heber College
Tiruchirappalli, Tamil Nadu
India
Shantanu Raj
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India

List of Contributors xxv
Gourav Rakshit
Department of Pharmaceutical Sciences &
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Sanket S. Rathod
Department of Pharmaceutical Chemistry
Bharati Vidyapeeth College of Pharmacy
Kolhapur, Maharashtra
India
Sharanya Roy
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Sneha Roy
Department of Bioinformatics
University of North Bengal
Darjeeling, West Bengal
India
Mithun Rudrapal
Department of Pharmaceutical Sciences
School of Biotechnology and Pharmaceutical
Sciences
Vignan’s Foundation for Science, Technology
& Research
Guntur, Andhra Pradesh
India
Biprajit Sarkar
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Anupam Sarma
Advanced Drug Delivery Laboratory
School of Pharmaceutical Sciences
Girijananda Chowdhury University
Guwahati
India
Ashish Shah
Department of Pharmacy
Sumandeep Vidyapeeth Deemed to be University
Vadodara, Gujarat
India
Afzal Basha Shaik
Department of Pharmaceutical Sciences
School of Biotechnology and Pharmaceutical
Sciences
Vignan’s Foundation for Science, Technology
and Research
Guntur, AP
India
Tripti Sharma
Department of Pharmaceutical Chemistry
School of Pharmaceutical Sciences
Siksha ‘O’ Anusandhan (Deemed to be University)
Bhubaneswar, Odisha, India
School of Pharmaceutical Sciences and Research
Chhatrapati Shivaji Maharaj University
Navi Mumbai, Maharashtra
India
Sonali S. Shinde
Department of Pharmaceutical Chemistry
Dr. D. Y. Patil Institute of Pharmaceutical
Sciences and Research
Pune, Maharashtra
India
Irum Siddiqui
IIRC-1, Department of Bioengineering
Integral University
Lucknow, Uttar Pradesh
India

List of Contributorsxxvi
Aditi Singh
Division of Biochemistry
School of Life Sciences
JSS Academy of Higher Education & Research
Mysuru, Karnataka
India
Department of Biotechnology
JSS Science and Technology University
Mysuru, Karnataka
India
Kratika Singh
Department of Microbiology
King George Medical University
Lucknow, Uttar Pradesh
India
Nisha Kumari Singh
Department of Pharmaceutical Sciences and
Technology
Birla Institute of Technology
Ranchi, Jharkhand
India
Urmila Singh
Department of Microbiology
King George Medical University
Lucknow, Uttar Pradesh
India
Ashapurna Sinha
Department of Biosciences
Integral University
Lucknow, Uttar Pradesh
India
Kamma Harsha Sri
Department of Pharmaceutical Sciences
Vignan Pharmacy College
Guntur, AP
India
Shivananju Nanjunda Swamy
Department of Biotechnology
JSS Science and Technology University
Mysuru, Karnataka
India
Vaishnavi Thakur
School of Biotechnology and Bioinformatics
D Y Patil Deemed to be University
Navi Mumbai, Maharashtra
India
Rajiv Kumar Tonk
Department of Pharmaceutical Chemistry
Delhi Pharmaceutical Sciences and Research
University
New Delhi
India
Sridhar Vemulapalli
Department of Pharmaceutical Sciences
University of Nebraska Medical Center
Omaha, NE
United States
Vivek Yadav
Department of Pharmaceutical Chemistry
Delhi Pharmaceutical Sciences and Research
University
New Delhi
India
Neha Zachariah
Swami Shraddhanand College
University of Delhi, New Delhi
India

xxvii
The growing incidence of diseases has created a dire need for proper medications, which are
readily available, potent and, cost-effective. To meet up this demand, the discovery of novel drug
molecules that would be clinically effective and safe is need of the hour. Traditionally, drug
discovery requires many years of development and a huge expenditure. However, using modern
computational approaches a drug molecule can be identified by investing less time at reduced cost
of discovery. It facilitates scientists to find potent and safe therapeutic molecules in more rational
way as compared to conventional approaches. In computational drug discovery, various tools,
methods/techniques, and software are used in designing better drug molecules with predictive
modeling and biophysical studies ranging from physicochemical/ADMET assessments, under-
standing the molecular mechanisms and toxicity screening. The computational approaches and
methods in drug designing include structure-based methods, ligand-based methods, virtual
screening, predictive analytics, informatics tools, artificial intelligence (AI) approaches, machine
learning methods, and multi-database methods.
This book mainly delves into recent advances in drug designing tools and techniques and their
applications pertaining to the discovery of novel therapeutic molecules. It includes armamentar-
ium of latest approaches and advanced methodologies available for drug design and their practical
applications in diverse cutting-edge therapeutic areas including cancer, multidrug-resistant
bacterial infections, neurodegenerative disorders, inflammatory diseases, and viral infections.
It comprises twenty-three chapters in unique topics contributed from experts all around the globe.
Some of the key features of the book are as follows:
● Computer-assisted methods and tools for structure- and ligand-based drug design, virtual screen-
ing and lead discovery, artificial intelligence and machine learning approaches for drug design
and ADMET and physicochemical assessments
● In silico and pharmacophore modeling, fragment-based design, de novo drug design and scaffold
hopping, network-based methods and drug discovery
● Rational design of natural products, peptides, enzyme inhibitors, drugs for neurodegenerative
disorders, anti-inflammatory therapeutics, antibacterials for multidrug-resistant infections, and
antiviral and anticancer therapeutics
The content of the book has been crafted in such a way that it would serve as useful resource
materials for postgraduate and doctoral courses in multidisciplinary academic and/or research
disciplines such as Pharmaceutical Sciences, Medicinal Chemistry, Pharmacology, Biomedical
Sciences, Biochemistry, Microbiology, Biotechnology, Drug Discovery, Computational Drug
Design, and Allied Sciences. This book will be particularly useful to drug developers, pharmaceutical
scientists (R&D), discovery scientists, biomedical scientists, healthcare professionals, biochemists,
Preface

Prefacexxviii
medicinal chemists, pharmacologists, research students, professors, and other researchers
working in the field of drug design and discovery. In addition, scientists involved in diverse areas
of medicinal chemistry, molecular modeling, drug design and discovery, computational drug
discovery (CADD), in silico drug discovery. and related areas are also expected to be the wider
audience, users, or readers of this book, regardless whether they are engaged in basic (chemistry,
computational chemistry, biophysical techniques, molecular modeling, and drug design) or
applied (such as pharmaceutical, medicinal chemistry, and drug discovery) research.
I extend my heartfelt gratitude to the contributors and publication team of Wiley for their invalu-
able contributions and tireless efforts, supports, and dedication, which have enabled the successful
compilation of the book volume.
October, 2024
Mithun Rudrapal
Guntur, India

1
1.1 Introduction
1.1.1 What Is Molecular Modeling?
Molecular modeling is at the cutting edge of scientific innovation and discovery, providing a
paradigm-shifting method for comprehending the microscopic world of atoms and molecules.
Researchers in various disciplines, including chemistry, biology, materials science, and pharmacology,
are given newfound strength by this computational technique that enables them to precisely model,
examine, and forecast the behavior of molecules and molecular systems. At its core, molecular mode-
ling uses computers’ computational capacity to reveal the nanoscale world’s well-kept secrets, providing
knowledge that is essential for expanding our understanding of the natural world and fostering techno-
logical advancements [1]. The attraction of molecular modeling is its capacity to connect theory and
experiment. It offers a comprehensive understanding to the researchers to examine the dynamics of
large molecular assemblies, the creation of chemical bonds, and the delicate description of atoms.
Researchers can now examine issues that are frequently difficult, expensive, or even impractical to solve
using only conventional experimental methods but much easier by this computational playground [2].
The significance of molecular modeling is most notable in drug development, where it has com-
pletely changed how pharmaceutical molecules are created and optimized. Researchers can
quickly screen and prioritize prospective treatments by modeling the interactions between drug
candidates and their target proteins, considerably speeding up the drug development process. This
has contributed to personalized medicine, in which treatments are matched to specific genetic
profiles as well as the development of novel therapeutics. The design of novel materials with spe-
cific features is aided in materials research by molecular modeling. Computational modeling
directs the development of materials for various applications, from electronics to aerospace,
whether it is optimizing the structure of innovative polymers, investigating the behavior of sophis-
ticated composites, or comprehending the properties of nanomaterials [3, 4].
Beyond these areas, molecular modeling provides a flexible tool for understanding chemical
processes, researching the principles of protein folding, and evaluating environmental effects. It is
essential to education because it aids in the visualization of intricate molecular relationships and
1
Molecular Modeling and Drug Design
Monalisa Kesh
1
, Abhirup Ghosh
2
, and Diptanil Biswas
3
1
Centre for Digital Health, Indian Institute of Technology Bombay, Mumbai, India
2
School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
3
Department of Statistical Genomics, National Institute of Biomedical Genomics, Kalyani, West Bengal, India

2
structures, which deepens comprehension of the underlying ideas that control the natural world.
By enabling researchers to solve the riddles of molecules and molecular systems, molecular mod-
eling acts as a light of scientific discovery in this era of computational inquiry, advancing us to new
horizons of knowledge and innovation [5].
1.1.2 Software Used for Molecular Modeling
In the discipline of molecular modeling, software is crucial because it allows researchers to carry
out intricate simulations, see molecular structures, and quickly process data. There are numerous
software programs accessible, each suited to particular modeling methodologies and study
goals [6]. These famous applications are frequently used for molecular modeling.
1.1.2.1 Schrodinger
For molecular modeling and drug development, Schrodinger provides a complete range of soft-
ware tools. A user-friendly interface is offered by Maestro, one of its flagship products, for various
modeling activities, including molecular dynamics (MD) simulations, virtual screening, and
structure-based drug creation [7].
1.1.2.2 GROMACS
GROMACS is a potent simulation tool for MD that is typically employed to examine the behavior
of biomolecules like proteins and lipids. It is a well-liked option in both academia and business
because of its speed and scalability [8].
1.1.2.3 Amber
Amber is a second extensively used program for modeling MD, with a significant emphasis on
biomolecular systems. It is appropriate for various research applications since it has tools for mod-
eling proteins, nucleic acids, and tiny compounds [9].
1.1.2.4 CHARMM
Known for its prowess in simulating intricate biomolecular systems and researching protein–
ligand interactions, CHARMM (Chemistry at HARvard Molecular Mechanics) is a leading name
in the field. Drug discovery and structural biology both make substantial use of it [10].
1.1.2.5 AutoDock
A well-liked molecular docking program, AutoDock, forecasts how tiny compounds will interact
with protein receptors. It is useful for identifying prospective medication candidates during virtual
screening [11].
1.1.2.6 VMD
The versatile molecular visualization program VMD (visual molecular dynamics) is used to exam-
ine and display molecular structures, trajectories, and data from numerous simulation programs.
It is especially helpful for producing gorgeous molecular graphics [12].
1.1.2.7 PyMOL
PyMOL is a popular molecular visualization program that provides an easy-to-use interface for
developing professional-grade 3D molecular images and animations. It is helpful for both aca-
demic and research endeavors [13].
1.1.2.8 Open Babel
A useful tool for data preparation and software package compatibility, Open Babel, is an open-
source chemical toolkit that enables users to convert between multiple chemical file formats [14].
Соседние файлы в папке Библиотека им академика М.И. Перельмана
