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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

3.3 lassification of Novel Drug Targets 53
3.3 Classification of Novel Drug Targets
Drug target identification is a crucial stage in drug discovery as it enables the researchers to trace
the mechanism of action of drug molecules and also ensures an efficient understanding of the
cryptic DTIs [4]. Selection of the right drug target is essential as majority of drug failure in the
R&D of the therapeutic industry is attributed to lack of drug efficacy. Proteins were initially con-
sidered major druggable targets that could be modulated by drugs such as small molecular weight
chemical compounds or biologics such as an antibody or recombinant protein [5]. Almost all
approved drugs are bound to protein targets and about 80% of the targets were either enzymes or
receptors. The protocol for lead identification also follows Lipinski’s rule of 5 that is valid only for
protein drug targets. However, the number of protein drug targets is limited as only 600 disease-
modifying protein targets exist out of 25,000 genes that is encoded by the human genome [10].
Therefore, the focus on target selection has now shifted to other biomolecules that serve as novel
drug targets. Macromolecules including enzymes and cellular receptors along with novel targets
like cytokines, DNA, RNA, chaperones, antigens, peptides, TFs, and transporters are being
exploited for drug discovery.
3.3.1 Transcription Factors
TFs constitute the most essential classes of proteins that bind to specific sites on DNA and play a
fundamental role in regulation of genome. TFs have been found to be associated with diseases and
thus are capable of being a therapeutic target. Although it is an interesting target, only a few TFs
are targeted by small molecules due to the absence of small-molecule binding pocket and intrinsic
disordered arrangement of TFs [11]. Novel TFs are emerging as a drug target for cancers, cardio-
vascular diseases (CVDs), and COVID-19. NF-κB is a TF found to play an important role in patho-
genesis of atherosclerosis. Vinpocetine and metformin are certain novel small molecules that are
under preclinical trials that target NF-κB [12]. Autophagy is a lysosome-dependent catabolic pro-
cess that takes place during starvation and helps in breaking down the product to form energy. It
also plays an important role in lipid metabolism by shuttling the lipid droplets to lysosomes to form
free fatty acids and glycerol [13]. During tumor growth, the process of autophagy is notably upreg-
ulated making it a therapeutic target for cancer. Transcription factor EB (TFEB) is the key regula-
tor for lysosomal biogenesis and autophagy. TFEB is found to be highly expressed in multiple types
of cancer, thus making it a therapeutic target for cancer. US FDA-approved drug eltrombopag is
used for the treatment of thrombocytopenia and is a potent TFEB inhibitor. Eltrombopag binds to
TFEB and creates a physical barrier between TFEB and the DNA, thus impeding the regulatory
function of TFEB. It binds to the basic helix–loop–helix–leucine zipper domain of TFEB, targeting
the bottom surface of the helix–loop–helix region. This interaction interferes with DNA recogni-
tion, leading to disruption of TFEB–DNA interaction. The way eltrombopag interacts with TFEB
does not cause any severe neurological effects, indicating its potential as an autophagy inhibitor.
Eltrombopag has been found to block autophagy and increase sensitivity to temozolomide in glio-
blastoma cells [14, 15].
3.3.2 Cytokines
Cytokines constitute 50% of ligands targeted by FDA-approved drugs and 40% of the novel ligands
for which agents are in development. Cytokines have been approved as drug targets because of
their specificity, size, and target modulation. In particular, they are soluble ligands that can be
targeted by fusion proteins, mAbs, and aptamers that can block protein ligand interaction eliciting

3 Novel Drug Targets for Small Molecule-based Drug Discovery54
therapeutic response [16]. Several novel cytokine targets such as interleukin-6 (IL-6) were identi-
fied in inflammatory cardiomyopathy that were responsible for myocardial infiltration, tissue deg-
radation, and T-cell activation. Novel cytokines that were previously not associated with
cardiomyopathies included COLEC 12 – a scavenger receptor that plays a role in host defense,
LAIR1 – a protein expressed on human leukocytes, LILRB4 – a leukocyte immunoglobulin-like
receptor, and FSTL3 was recently identified as a relevant prognostic marker for major adverse car-
diovascular events in stable coronary artery disease [17]. IL-17 is an important drug target for
autoimmune diseases like psoriasis. Two molecules targeting IL-17 namely secukinumab and
ixekizumab have been approved by FDA in 2015 and 2016, respectively. IL-23 regulates the T-helper
cells that produce IL-17; therefore, IL-23 is also a target for a wide range of mAb drugs like
guselkumab, tildrakizumab, and risankizumab approved in 2017, 2018, and 2019, respectively, for
the treatment of psoriatic disease and other immune-inflammatory disease [16]. Cytokine storm
syndrome (CSS) is an implication of COVID-19 prognosis that leads to dysregulation of synthesis
of cytokines. IL-6 plays an important role in the pathogenesis of CSS. The significant role of IL-6 in
pathogenesis of COVID-19 was confirmed in a range of studies, which showed that the plasma
concentration of IL-6 was increased in patients with severe COVID-19. IL-6 inhibitors like tocili-
zumab and sarilumab suppress the overactive cytokine in COVID-19-affected individuals but are
used as an off-label drug [18].
3.3.3 Chaperones
Molecular chaperones play an important role in the proteostasis network. Proteostasis network
maintains the health of the proteome by controlling protein folding, misfolding, intracellular pro-
tein transport, protein aggregation, and trafficking. Dysregulation of this network leads to various
neurodegenerative diseases and can also be a probable cause of cancer. Two of the most significant
chaperone families, heat shock protein 70 kDa (Hsp70) and 90 kDa (Hsp90), rely on adenosine
triphosphate (ATP) hydrolysis for energy and cooperation with their co-chaperones to create the
dynamic complex [19]. Hsp-90 is an evolutionary conserved molecular chaperone that plays a
major role in cell signaling; therefore, it attracts cancer cells to prevent oncoprotein mutation and
degradation and is thus regarded as a potential anticancer target. Inhibition of Hsp-90 triggers the
release of heat shock inhibition factors, Hsp70 and Hsp27 chaperones that can degrade normal
proteins along with oncoproteins, thus reducing Hsp-90 inhibitor efficacy. Therefore, a protein
kinase cochaperone CDC37 that promotes oncogenesis on interacting with Hsp-90 is chosen as a
potential therapeutic target as the anticancer drugs acting on this protein–protein interaction (PPI)
system destroy the oncoproteins specifically retaining the conformational integrity of normal pro-
teins. The inhibition of these PPIs is facilitated by novel small-molecule drugs such as DDO-5942.
NEN (FDA-approved antihelminthic drug) was proved to be an efficient drug against hepatocel-
lular carcinoma by inhibiting CDC37 interactions and DCZ3112 that were found to disrupt PPI
and degrade the Hsp-90 client proteins [20].
Small molecular chaperones are found to be potential stabilizers of the transthyretin–Aβ interac-
tion. Transthyretin is a homotetrameric protein that prevents aggregation of amyloid beta peptides,
thus inhibiting the prognosis of Alzheimer’s disease. Being a tetramer, its stability is a key factor in
interacting with Aβ peptide as the degradation of transthyretin to a monomeric form leads to amy-
loid aggregation. Therefore, small-molecule chaperones such as sulindac, olsalazine, flufenamic
acid (marketed drugs), and luteolin (investigational drug) are used as novel therapeutic drugs to
stabilize transthyretin–Aβ interaction [21].

3.3 lassification of Novel Drug Targets 55
3.3.4 Viral Targets
The emergence of various viral infections such as HIV, hepatitis, HPV infection, and the recent
emergence of severe acute respiratory syndrome (causative agent being coronavirus) has led to the
investigation of novel drug targets to combat the same. Antiviral drugs mostly comprise small
molecules that target the viral cellular machinery. HIV that is responsible for causing AIDS is a
deadly viral infection that was countered with Isentress approved by FDA in 2007 as the first inte-
grase inhibitor that blocks integration of viral DNA with the host genome. The first peptide (36
amino acids)-based HIV inhibitor to be approved was enfuvirtide (Fuzeon) in 2003. Enfuvirtide
imitates the helix in heptad region 2 of the viral glycoprotein 41 (GP41) to prevent HIV from fusing
with the host cell membrane. Ibalizumab (Trogarzo), a monoclonal antibody that attaches to T-cell
surface CD4 receptors to stop HIV from entering and reproducing, was approved in 2018.
Dolutegravir and lamivudine (Dovato), as a two-drug combination, were authorized by the FDA in
April 2019 to treat HIV-1 infection. Dolutegravir, an integrase strand transfer inhibitor (INSTI)
INSTI, and lamivudine, an nucleoside reverse transcriptase inhibitor (NRTI), work together to
inhibit HIV-1 replication and protease activity [22].
The severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is the cause of the COVID-19
pandemic, which is continuing to spread quickly around the world. More than 16.2 million confirmed
cases from 213 countries were recorded as of July 26, 2020. Deaths have exceeded 648,866 to
date [23]. SARS-CoV-2 belongs to the family Coronaviridae, which also includes the virulent
strains SARS-CoV and Middle East respiratory syndrome CoV (MERS-CoV) that infect both
humans and animals [24]. Remdesivir triphosphate (RDV-TP) is a direct antagonist of ATP and
blocks the function of RNA dependant RNA polymerase (RdRP) (facilitates viral replication) when
it takes its place upon elongation of the positive sense RNA of the virus by RdRP. Therefore, RdRP
is considered a potential drug target to counter the virus. Favipiravir is also another inhibitor of
RdRp that suppresses replication of SARS-COV-2 RNA [25]. Ivermectin is another FDA-approved
drug that shuts down the ability of transporter importin to transport viral proteins into the nucleus
of the host [26].
3.3.5 G Protein-coupled Receptors
GPCRs are a class of seven-transmembrane proteins that have garnered significant attention in
academia and the pharmaceutical industry for many years due to their physiological and therapeu-
tical significance as well as their accessibility for small-molecule drug discovery. GPCRs represent
the largest family of druggable proteins in the human genome and unsurprisingly are targeted by
more than 30% of marketed drugs worldwide [27]. Recent developments in GPCR structure illumi-
nation reveal that ligands may bind to distant allosteric sites in addition to the conventional orthos-
teric site, opening up a wide range of novel possibilities for drug discovery [28]. The contribution
of GPCRs to the druggable genome has been approximated in earlier research. We have discovered
134 GPCRs that are targets for presently licensed medications, namely FDA-approved medica-
tions, by gathering data from open sources. About two-thirds of currently druggable GPCRs con-
trol cAMP, and many drug targets linked to GPCRs affect cAMP signaling (e.g., phosphodiesterases
(PDEs) or proteins associated with ligand synthesis/transport/regulation whose receptors control
cAMP synthesis). As a result, the cAMP pathway is probably the signaling route that currently
approved treatments target the most frequently [29]. Phosphodiesterase-4 (PDE4) is a key enzyme
that breaks down cyclic 3′,5′-adenosine monophosphate (cAMP, or cyclic AMP), an extremely
effective inhibitor of PDE4 is roflumilast that increases intracellular cAMP. Roflumilast was

3 Novel Drug Targets for Small Molecule-based Drug Discovery56
approved by FDA in 2018 to manage chronic obstructive pulmonary disease as per PubChem data.
Tiotropium, a long-acting muscarinic receptor antagonist, was included in 2015 in the Global
Initiative for Asthma (GINA) guidelines. It is the only LAMA that is presently licensed for the
treatment of asthma, whereas aclidinium, glycopyrronium, tiotropium, and umeclidinium
are approved for the treatment of COPD [30]. Calcitonin gene-related peptide (CGRP) receptors
are majorly exploited to manage migraines. Some novel small molecules targeting these receptors are
Ubrogepant and Rimegepant that was approved by FDA in 2019 and 2020, respectively [31].
Class A GPCR CC chemokine receptor 5 interacts with the viral glycoprotein gp120 of HIV to
form a co-receptor complex (PPI) that is responsible for the infectivity of the virus. Maraviroc is an
anti-HIV drug that can disrupt CCR5-gp120 interaction by attaching to CCR5 allosterically; there-
fore, it acts as a chemokine agonist and inhibitor of GPCR [32].
3.3.6 Transporters
Transporters are proteins that traverse membranes helping move ions and biological substances
across the phospholipid bilayer. Solute carrier (SLC) transporters and ATP binding cassette (ABC)
transporters are two primary kinds of transporters. SLC transporters are involved in passive trans-
port, whereas ABC transporters are involved in active transport. Cystic fibrosis transmembrane
transporter coded by CFTR gene belongs to ABCC7 class of molecules [33]. Tezacaftor is a novel
CFTR modulator belonging to the class of correctors that was approved by FDA in 2018. Correctors
target the defectively folded protein due to the most common CFTR mutation F508del. Tezacaftor
improves processing and trafficking of F508del-CFTR and increases chloride transport from 2.8%
to 8.1% in bronchial epithelium [34]. Neonatal FcRn is another transcytosis receptor that is coded
by FCGRT gene that mediates transport of IgG from mother to infant as proposed by Brambell in
the 1960s. In autoimmune disorders like myasthenia gravis, pathogenic IgG autoantibodies are
produced that bind to immune antigenic checkpoints and organ targets leading to organ damage.
FcRn was recognized as a novel yet crucial transporter that is responsible for increasing the half-
life of IgG autoantibodies in plasma by salvaging it from lysosomal degradation [35]. Therefore,
FcRn targeting small-molecule drugs like Efgartigimod alfa, which is a monoclonal antibody acts
as a potential therapeutic approved by FDA [36].
3.3.7 Enzymes
Protein phosphorylation plays an important role in regulation of metabolic processes. Protein
kinases are a group of enzymes that carry out phosphorylation and dysregulation of these enzymes
plays an important role in development of autoimmune, inflammatory, cardiovascular, and neu-
rological disorders. Protein kinases are the second most prominent drug targets after GPCRs [37,
38]. The search for new treatments for various diseases like CVDs and cancer has led to the dis-
covery of novel protein kinase targets. Recent studies have shown Janus kinase, which is acti-
vated by various cytokines, to be a prominent target in treatment of Systemic Lupus Erythematosus
(SLE) [39]. Tofacitinib which targets JAK 1/3 is a US FDA-approved drug for RA and ulcerative
colitis, which showed good results during phase I and I trials. Brepocitinib selectively inhibits
JAK1/Tyk2 and shows a promising treatment in SLE. It is in phase II trials targeting nonrenal
SLE patients [40]. DRAK1 is a negative regulator of TRAF6 protein, which is involved in inflam-
matory pathways that induce tumorigenesis. Thus, targeting DRAK1 can be used as a therapeutic
target to antagonize TRAF6 protein overexpression [41]. Cell death is an essential process that

3.4 Small Molecules as Drugs 57
helps in maintaining development and homeostasis of an organism. Apoptosis is the process of
programmed cell death, which is important for various cellular processes. Cell necrosis, unlike
apoptosis, is the process of unregulated cell death that happens due to external stress either physi-
cal or chemical. Necroptosis is a signal transduction pathway that regulates the process of pro-
grammed necrosis via the RIPK1 class of kinase protein and its downstream mediators:
RIPK3 kinase and pseudokinase MLKL [42, 43]. RIPK1 is the central regulator of necroptosis,
which is carried out via its kinase and scaffolding activities [44]. Thus, RIPK1 has become an
important drug target for various neurodegenerative, autoimmune, and inflammatory diseases.
GFH312 is a small molecule that inhibits RIPK1 and shows an antinecroptosis effect in neurologi-
cal and inflammatory disease models [45]. GFH312 is now under phase II trials for inflammatory
conditions after proving to be safe in the phase I trials.
ATG4 is a cysteine protease required for autophagosome formation. It has four homologs that
are ATG4A, ATG4B, ATG4C, and ATF4D. ATG4B is overexpressed in tumor cells of colorectal
cancer, glioblastoma, gastric cancer, and other cancers. Advanced gastric cancer is usually fatal
due to lack of potential therapeutics. Azalomycin F4a is found to tightly bind and inhibit ATG4B,
thus blocking the metastatic progression of advanced gastric cancer and sensitizing tumors to
chemotherapy [46]. S130 is a novel molecule that is found to bind tightly to ATG4B and inhibits
only ATG4B and not to other proteases [47].
3.3.8 RNA Targets
RNA plays an important role in the transfer of information in biological systems; thus, it can be an
important therapeutic target for small molecules. Most of the small molecules available are target
proteins; among these proteins, some disease-related proteins are termed as undruggable. This
problem could be solved by directly targeting mRNAs, which can modulate the proteins before
their biogenesis [48].
70% of the human genome is pervasively transcribed into ncRNAs; therefore, it acts as an emerg-
ing future target for drug discovery. The FDA approved the first direct RNA-splicing modifier for
the treatment of spinal muscular atrophy (SMA) in 2020 using the smallmolecule risdiplam. Exon
7 inclusion and increased expression of the motor neuron protein are facilitated by risdiplam’s
direct binding to the survival of the mRNA-spliceosome complex of motor neuron 2 (SMN2) [49].
3.4 Small Molecules as Drugs
Small molecules are referred to as compounds that have a molecular weight of <900 Da. 90% of the
drug molecules available in the pharmaceutical industry are small molecules [50]. These mole-
cules are capable of affecting functions of various proteins by forming complexes with their tar-
gets, thus affecting the PPIs [51]. For more than a century, small molecules have been providing
medical breakthroughs for human diseases. The features that make small molecules a good drug
molecule are oral bioavailability, modular structure, and their ease of accessibility via chemical
synthesis. It typically does not show immunogenicity and can bind to targets with high selectivity.
The metabolic stability of small molecules ranges from low to high. Other than these features, it
also shows high shelf life stability and is compatible with different drug formulations and admin-
istration, thus proving to be a good drug molecule [52]. Table 3.1 consists of some of the small-
molecule drugs that interact with novel therapeutic target.

Table 3.1 Small molecule drugs targeting novel drug targets.
Small molecule
drug Nature of drug Drug target PDB ID Target class Indication Approval status
Eltrombopag Small-molecule
nonpeptide TPO-R
agonist
TF-EB 7Y62 Transcription
factor
Glioblastoma Preclinical trials
Guselkumab mAb IL23 3DUH Cytokine Psoriatic disease and
immune-inflammatory
disorder
FDA-approved
NEN
(Niclosamide
ethanolamine)
Small-molecule
inhibitor
CDC37-DCZ112 PPI 2W0G Chaperone Hepatocellular
carcinoma
FDA-approved –
antiheminthic
drug
Flufenamic
acid, Luteolin,
Olsalazine
Small molecular
chaperones
Transthyretin-amyloid-β
aggregation
6C3T Protein–
protein
interaction
Alzheimer’s disease Preclinical trials
Remdesivir
triphosphate
(RDV-TP),
Favipiravir
Small-molecule
adenosine analog
Small-molecule
substrate analog
RNA-dependent RNA
polymerase
6M71 Viral enzyme COVID-19 FDA-approved
Roflumilast Small-molecule
long-acting inhibitor
PDE-4
(phosphodiesterase-4)
4NW7 Enzyme Chronic obstructive
pulmonary disease
(COPD)
FDA-approved
Tiotropium Small molecule
antagonist (LAMA)
Long-acting
M3 muscarinic
acetylcholine receptor
4DAJ GPCR Asthma FDA-approved
Ubrogepant,
Rimegepant
Small-molecule
inhibitor
Calcitonin gene-related
peptide receptor (CGRPR)
5N7S GPCR Migraine FDA-approved
Maraviroc Small-molecule
inhibitor
CCR5 (allosteric
inhibitor)
4MBS GPCR HIV infection FDA-approved
Tezacaftor Small-molecule
CFTR corrector
Misfolded CFTR due to
F508 del mutation
1XMI ABC
transporter
Cystic fibrosis FDA-approved

Efgartigimod
alfa
mAb Neonatal FcRn receptor 1I1A Transcytosis
receptor
Myasthenia gravis FDA-approved
Tofacitinib Small-molecule
inhibitor
JAK 1/3
a
6N7A
a
6NY4
Enzyme SLE Phase II trials
Brepocitinib Selective inhibitor JAK1/Tyk2
a
6N7A
a
6X8G
Enzyme SLE Phase II trials
GFH312 Small-molecule
inhibitor
RIPK1
a
6NW2 Enzyme Inflammation Phase II trials
Azalomycin
F4a
Selective inhibitor ATG4B 2D1I Enzyme Advanced gastric
cancer
Preclinical
study
Risdiplam Small-molecule
mRNA splicing
modifier
mRNA spliceosome
complex of motor neuron
2 (SMN2)
5OR0 Long
noncoding
RNA
Spinal muscular
atrophy
FDA-approved
a
The structures present are complex with another molecule.

3 Novel Drug Targets for Small Molecule-based Drug Discovery60
3.5 Conclusion
The discovery and identification of novel targets and drugs is the current requirement of the phar-
maceutical industry as well as in biomedical research [53]. Novel therapeutic drug target identifi-
cation is a tedious, intensive, and complex approach that demands integration of various
biochemical, genetic, and bioinformatics tools and information. Identification of effective novel
drug target serves as a potential progress in the drug discovery process. Recent advancements in
the target identification approach have enabled researchers to focus on nonprotein molecules as
novel targets apart from the age-old extensively exploited protein targets. Small-molecule drugs
have been used as potential targeting molecules for druggable targets over the years. Small mole-
cules hold advantages in the drug market due to their bioavailability, target specificity, simpler
manufacturing and characterization, and nontoxic and nonimmunogenic properties. However, the
exploitation of small molecules has made many targets undruggable to small molecules as they are
merely biological mimics. As stated by Zhong et al. [54], crizotinib, a small-molecule drug inhibi-
tor of ALK in various cancer types, showed drug resistance in patients with L1196M and G1269A
mutation within 12 months post-administration in randomized phase 3 trials. Many cancer and
bacterial cells have developed small-molecule drug resistance; therefore, small molecule fails to be
a cosmopolitan drug (Table 3.2).
Table 3.2 Structure of small molecules.
Small molecule Structure Pubchem CID
Eltrombopag
O
O
H
N
NH
N
N
O
O
H
135449332
NEN
Cl
Cl
N
N
+
H
O
O
O
O
–
H
H
H
O
H
N
14992

Table 3.2 (Continued)
Small molecule Structure Pubchem CID
Flufenamic
acid
O
O
H
N
F
F
F
H
3371
Luteolin
H
H
O
O O
O
O
O
H
H
5280445
Olsalazine
O
N
N
H
O
O
O
O
O
H
H
H
22419
Remsedivir
triphosphate
H
N
N
N
N
N
O
O
O
O
O
O
O
O
O
P
H
H
O
O
H
O
O
H
P
P
C
H
H
H
56832906
(Continued)

3 Novel Drug Targets for Small Molecule-based Drug Discovery62
Table 3.2 (Continued)
Small molecule Structure Pubchem CID
Favipiravir
H
H
O
O
F
N
N
N
H
492405
Roflumilast
N
N
H
Cl
O
O
O
F
F
Cl
449193
Tiotropium
O
O
H
H
O
N
+
O
H
S
S
5487427
Ubrogepant
F
F
F
N
N
N
O
N
N
H
H
O
O
68748835
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