Foreword
Preface
Acknowledgement
Contents
About the Editor
1: Computational Approaches in Drug Discovery and Design
1.1 Introduction
1.2 Structure-Based Drug Designing
1.2.1 Target Identification
1.2.2 Modeling and Visualization of Macromolecule Structure
1.2.3 Binding Site Prediction and Analysis
1.2.4 Molecular Docking
1.2.4.1 Flexible Docking
1.2.4.2 Rigid Docking
1.2.5 Structure-Based Virtual Screening
1.2.6 Validation of Molecular Docking
1.3 Ligand-Based Designing
1.3.1 Pharmacophore Modeling
1.3.2 Quantitative Structure-Activity Relationship (QSAR)
1.3.2.1 CoMFA
1.3.2.2 CoMSIA
1.4 Computation of HOMO and LUMO Energy
1.5 ADMET Prediction and Analysis
1.6 Molecular Dynamics Simulation
1.7 Identification of New Drug-Like Molecules for Hyperuricemia from Millets: A Case Study
1.8 Discovery and Designing of Natural Lead Compounds for Liver Cancer: A Case Study
1.9 Examples of Drugs Synthesized Using CADD
1.10 Success and Limitations
1.11 Conclusion
References
2: Molecular Modeling of Proteins: Methods, Recent Advances, and Future Prospects
2.1 Introduction
2.1.1 Amino Acids
2.1.2 Basic Principles of Protein Structure
2.2 Explosion of Protein Related Data
2.3 Protein Structure Determination
2.3.1 X-Ray Crystallography
2.3.2 NMR Spectroscopy
2.3.3 3D Electron Microscopy
2.4 Protein Structure Prediction
2.4.1 Homology or Comparative Modeling
2.4.1.1 Template Recognition and Initial Alignment
2.4.1.2 Alignment Correction
2.4.1.3 Modeling Structurally Conserved Region (SCR) and Backbone Generation
2.4.1.4 Loop Modeling
Knowledge-Based
Energy-Based
2.4.1.5 Side-Chain Modeling
2.4.1.6 Model Optimization
Quantum Force Fields
Self-Parameterizing Force Fields
2.4.1.7 Model Validation
2.4.2 Fold Recognition or Threading Method
2.4.3 Ab Initio Methods
2.5 Evaluation and Validation of Modeled Structure
2.6 Recent Advances in Prediction Approaches
2.7 Applications
2.8 Conclusion
References
3: Cavity/Binding Site Prediction Approaches and Their Applications
3.1 Introduction
3.2 Target Molecule
3.3 Binding Site and Active Site
3.4 Ligand Molecule
3.5 Binding Affinity
3.6 Chemical Specificity
3.7 Binding Site and Molecular Interactions
3.7.1 Protein-Drug Interactions
3.7.1.1 Reversible Binding
3.7.1.2 Irreversible Binding
3.7.1.3 Factors Affecting Protein-Drug Binding
3.7.1.4 Role of Water Molecules
3.7.2 Drug-Nucleic Acid Interactions
3.7.3 Protein-Protein Interactions
3.7.4 Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate
3.8 Binding Site Prediction
3.8.1 Evolutionary Algorithms/Sequence-Based Predictions
3.8.1.1 Single Residue Based Approach
3.8.1.2 Window Based Approach
3.8.2 Energy-Based Algorithms
3.8.3 Geometry-Based Algorithm/Structure-Based Predictions
3.9 Approaches
3.9.1 Statistical Approach
3.9.2 Machine Learning
3.9.3 Meta-Predictors
3.10 Prediction Tools and Servers
3.11 Validation of Binding Site
3.12 Role of the Binding Site in Drug Designing
3.13 Recent Advances and Future Perspective
3.14 Conclusion
References
4: Role of ADMET Tools in Current Scenario: Application and Limitations
4.1 Introduction
4.1.1 ADMET Prediction
4.1.2 ADMET Parameters and their Role
4.2 Importance of ADMET
4.3 The Evolving Science of ADMET
4.4 Blood-Brain Barrier Models
4.5 ADMET Prediction
4.6 Strategies for the Designing of ADMET Model
4.6.1 Selection of Experimental Data
4.6.2 Calculation of Physicochemical Parameters or Descriptor Values
4.6.3 ADMET Prediction Methods and Tools
4.6.3.1 Recursive Partitioning Regression
4.6.3.2 Partial Least Square (PLS) Regression
4.6.3.3 Random Forests (RF)
4.6.3.4 Decision Trees
4.6.3.5 Naive Bayes Classifiers
4.6.3.6 k-Nearest Neighbour (k-NN)
4.6.3.7 Support Vector Machine (SVM)
4.7 ADMET Tools
4.8 Challenges in Present Scenario and Future Prospective
4.9 Conclusions
References
5: Database Resources for Drug Discovery
5.1 Introduction
5.2 Therapeutic Target Information
5.2.1 Universal Protein Resource (UniProt)
5.2.1.1 UniProtKnowledgeBase (UniProtKB)
5.2.1.2 UniProt Reference Clusters (UniRef)
5.2.1.3 UniProt Archive (UniParc)
5.2.2 Protein Data Bank (PDB)
5.2.3 Molecular Modeling Database
5.2.4 Therapeutic Target Database
5.2.5 Herbal Ingredients Targets (HIT) Database
5.2.6 SuperTarget
5.3 Chemical Information
5.3.1 PubChem
5.3.2 Zinc
5.3.3 ChEMBL
5.3.4 Chemical Entities of Biological Interest (ChEBI)
5.3.5 NCI Database
5.3.6 ChemDB
5.3.7 ChemSpider
5.3.8 BindingDB
5.3.9 PDBbind
5.3.10 Toxin and Toxin-Target Database (T3DB)
5.3.11 BIAdb
5.3.12 Super Natural II
5.3.13 Naturally Occurring Plant-Based Anti-Cancer Compound-Activity-Target Database (NPACT)
5.3.14 Dictionary of Natural Products Online
5.3.15 Ligand Expo
5.3.16 SuperLigands
5.3.17 Toxicology Data Network
5.4 Drug Molecule Information
5.4.1 DrugBank
5.4.2 SuperDRUG2
5.4.3 PharmGKB
5.4.4 Search Tool for Interactions of Chemicals (STITCH)
5.5 Metabolomic Pathway Information
5.5.1 Kyoto Encyclopedia of Genes and Genomes (KEGG)
5.5.2 Human Metabolome Database (HMDB)
5.5.3 Small Molecule Pathway Database (SMPDB)
5.5.4 BiGG
5.5.5 MetaboLights Database
5.5.6 BioCyc
5.5.7 Reactome
5.5.8 WikiPathways
5.6 Disease and Physiology Information
5.6.1 Online Mendelian Inheritance in Man (OMIM)
5.6.2 METAGENE
5.6.3 RAMEDIS
5.6.4 Online Metabolic and Molecular Basis of Inherited Disease (OMMBID)
5.7 Peptide Information
5.7.1 PepBank
5.7.2 StraPep
5.7.3 Antimicrobial Peptide Database (APD)
5.7.4 CAMPR3
5.7.5 CancerPPD
5.8 Challenges and Future Perspective
5.9 Summary
References
6: Molecular Docking and Structure-Based Drug Design
6.1 Introduction
6.2 Docking Guidelines
6.2.1 Hardware and Software Requirements for Molecular Docking
6.2.2 Docking Process
6.2.3 Ligand and Protein Preparation
6.2.4 Ligand Conformations Strategies
6.2.5 Scoring Functions
6.2.5.1 Force Field
6.2.5.2 Empirical Scorings
6.2.5.3 Knowledge-Based Scoring
6.2.6 Ensemble Docking
6.2.7 Consensus Docking
6.3 Different Types of Docking Based on Interactions
6.3.1 Protein-Ligand Docking
6.3.2 Protein-Peptide like Ligand Docking
6.3.3 Protein-Protein Docking
6.3.4 Protein-Nucleic Acid Docking/Nucleic Acid-Ligand Docking
6.4 Water Solvation and Docking
6.5 Docking Tools
6.6 Virtual Screening
6.7 Analysis of Docking Results
6.8 Limitations of Docking Algorithms and Future Scope
6.9 Major Developments in Docking
6.10 Conclusion
References
7: Molecular Dynamics Simulation of Protein and Protein-Ligand Complexes
7.1 History and Background
7.2 Introduction
7.3 Principle of MD Simulation
7.3.1 Periodic Boundary Conditions
7.3.2 Ewald Summation Techniques
7.3.3 Particle Mesh Ewald Method
7.3.4 Thermostats in MD
7.3.5 Solvent Models
7.3.6 Energy-Minimization Methods in MD Simulations
7.4 Current Tools for MD Simulation
7.4.1 Recent Advances in Hardware to Run MD Simulation
7.4.2 GROMACS
7.4.3 AMBER
7.4.4 CHARMM-GUI
7.4.5 NAMD
7.4.6 Quantum-Mechanics/Molecular-Mechanics (QM/MM)
7.4.7 HyperChem
7.5 Other Advance Methods for MD Simulation
7.5.1 Metadynamics
7.6 Analysis of MD Trajectories Through GUI-Based Software
7.6.1 Visual Molecular Dynamics
7.6.2 PyMOL
7.6.3 Chimera
7.7 Structural Parameters for Analysis of MD Simulation
7.7.1 RMSD
7.7.2 RMSF
7.7.3 Radius of Gyration
7.7.4 Protein-Ligand Contacts
7.7.5 SASA
7.7.6 Principal Component Analysis or Essential Dynamics
7.7.7 Secondary Structure Analysis
7.8 Application of MD Simulation
7.8.1 Mutational Analysis
7.8.2 Application in the Drug Designing
7.8.2.1 Inhibitor Designing Against MtbICL
7.8.2.2 Inhibitor Designing Against Fasciola gigantica Thioredoxin Glutathione Reductase
7.8.3 Unfolding Studies
7.8.3.1 Urea Induced Unfolding of FgGST1
7.8.3.2 GdnHCl-Induced Unfolding Analysis
7.8.3.3 pH-Induced Effects on the Structure and Stability of the Protein
7.9 Conclusions
References
8: Computational Approaches for Drug Target Identification
8.1 Introduction
8.2 Drug Targets
8.3 Drug Target Identification
8.4 Computational Approaches for Drug Target Identification
8.5 Homology-Based Approaches
8.5.1 Human Homologs
8.5.2 Human-Microbiome Homologs
8.5.3 Essentiality
8.5.4 Virulence Factor Homologs
8.5.5 Drug Target Homologs
8.5.6 Cellular Location
8.5.7 Role in the Biological Pathway
8.5.8 Case Study: Subtractive Approach for Drug Target Identification
8.6 Network-Based Approaches
8.6.1 Centrality Based Drug Target
8.6.1.1 Hubs as Target
8.6.1.2 Betweenness Centrality Based Target
8.6.1.3 Mesoscopic Centrality Based Target
8.6.1.4 Weight-Based Drug Target
8.6.2 Limitations
8.7 Properties of an Ideal Drug Target
8.8 Druggability of Drug Target
8.8.1 Importance of Druggability
8.9 Computational Methods for Druggability Assessment
8.9.1 Sequence-Based Methods
8.9.2 Structure-Based Methods
8.9.2.1 Identifying Cavities and Binding Pockets
8.9.2.2 Druggability of Binding Pocket
Position of the Atoms
Cavity Size
8.9.2.3 Target Specificity Assessment
Sequence Alignment Based Assessment
Structure Alignment Based Assessment
8.9.3 Quantification of Druggability
8.9.4 Major Concern
8.9.4.1 Size of Training Sets
8.9.4.2 Binding Site Flexibility
8.10 Target-Based Drug Discovery
8.10.1 Multi-Target Drug Designing
8.10.1.1 Identification of a Set of Targets ``Multi-Targets´´
8.10.1.2 Generation of Multi-Target Pharmacophore
8.10.1.3 Virtual Screening
8.10.1.4 Generation or Selection of Multi-Target Compound
8.10.1.5 Evaluation and Optimization of Multi-Target Specific Compound
8.11 Summary
References
9: Computational Screening Techniques for Lead Design and Development
9.1 Introduction
9.2 High-Throughput Screening
9.2.1 Assay Design
9.2.2 Biochemical Assays
9.2.3 Whole-Cell Assays
9.2.4 Automatic Methods of Library Generation and Robotics in HTS
9.2.5 Profiling
9.2.6 Screening Expense and Outsourcing Screening
9.3 QSAR Theories
9.4 Molecular Descriptors Used in QSAR
9.5 Methods of QSAR
9.5.1 2D QSAR Methods
9.5.1.1 Free Energy Models-Hansch Analysis Linear Free Energy Relationship
9.5.1.2 Mathematical Model
Free Wilson Analysis
Statistical Methods
Discriminant Analysis
Cluster Analysis
9.5.1.3 Principal Component Analysis (PCA)
9.5.1.4 Quantum Mechanical Methods
9.5.2 3D-QSAR
9.5.2.1 Molecular Shape Analysis (MSA)
9.5.2.2 Self-Organizing Molecular Field Analysis (SOMFA)
9.5.2.3 Comparative Molecular Field Analysis (CoMFA)
9.5.2.4 Comparative Molecular Similarity Indices Analysis (CoMSIA)
9.5.2.5 3D Pharmacophore Modeling
9.5.3 4D-QSAR
9.5.4 5D-QSAR
9.5.5 4D vs 5D-QSAR
9.6 ADME Screening
9.6.1 Absorption
9.6.1.1 Biologic Factors
9.6.1.2 Passive Diffusion
9.6.1.3 Carrier-Mediated Facilitated Transport
9.6.1.4 Local Blood Flow
9.6.1.5 Gastric Emptying Time
9.6.1.6 pH-Partition Theory
9.6.1.7 Ion Trapping
9.6.1.8 Chemical Modifications Affect the Absorption
9.6.1.9 Optimizing Absorption
9.6.2 Distribution
9.6.2.1 Optimizing Distribution
9.6.3 Metabolism
9.6.3.1 Phase I
Oxidation
Reduction
Hydrolysis
9.6.3.2 Phase II-Conjugation
9.6.3.3 Factors Affecting the Metabolism of a Drug
9.6.3.4 Optimizing Metabolism
9.6.4 Excretion
9.6.4.1 Factors Affecting ADME Properties and Modeling Process
9.6.4.2 Drug Likeness
9.6.4.3 Lipophilicity
9.6.4.4 Solubility
9.6.4.5 Pharmacokinetic Process
9.7 Toxicological Screening
9.7.1 Acute Systemic Toxicity
9.7.2 Toxicological Endpoints
9.7.3 Structural Alerts and Rule-Based Method
9.7.4 Read Across Methods Using Chemical Category
9.7.5 Quantitative Structure Activity Relationship Model Using a Statistical Method
9.7.6 Organization for Economic Cooperation and Development (OECD) Guidelines
9.7.6.1 Optimizing Toxicity
9.8 Limitations and Future Scope
9.9 Conclusions
References
10: Advances in Pharmacophore Modeling and Its Role in Drug Designing
10.1 Introduction
10.2 Features in a Pharmacophore
10.3 Pharmacophore Modeling
10.3.1 Ligand-Based Pharmacophore
10.3.2 Building a Pharmacophore
10.3.2.1 Ligand Preparation
10.3.2.2 Pharmacophore Feature Mapping
10.3.2.3 Searching Common Pharmacophore
10.3.2.4 Scoring the Common Pharmacophore
10.3.3 Algorithms Used to Build a Pharmacophore
10.3.4 Structure-Based Pharmacophore
10.3.4.1 Redocking of Co-Crystal Ligand
10.3.4.2 Scoring for Pharmacophoric Sites
10.3.4.3 Building a Pharmacophoric Hypothesis
10.4 Tools for Pharmacophore Building
10.5 Validation of a Pharmacophore Hypothesis
10.6 A Case Study of Structure and Ligand-Based Pharmacophore
10.7 Uses of Pharmacophore
10.7.1 Virtual Screening
10.7.1.1 An Instance of Virtual Screening and Its Workflow
10.7.2 Pharmacophore Fingerprint
10.7.2.1 An Instance of Pharmacophore Fingerprint Searching
10.7.3 De Novo Ligand Design
10.7.3.1 An Instance of De Novo Ligand Design
10.8 Success Stories in Pharmacophore-Based Drug Designing
10.9 Significance of Pharmacophore
10.10 Downside of Pharmacophore Modeling
10.11 Conclusion
References
11: In Silico Designing of Vaccines: Methods, Tools, and Their Limitations
11.1 Introduction
11.1.1 Live Attenuated Vaccine
11.1.2 Inactivated Vaccine
11.1.3 Subunit Vaccine
11.1.4 Recombinant Vector and DNA Vaccines
11.1.5 Epitope-Based Vaccines
11.2 B and T Cell Epitopes
11.2.1 B Cell Epitopes
11.2.2 T Cell Epitopes and Their Processing
11.3 Bioinformatics in Vaccine Design
11.4 Prediction Tools for Class I and II MHC Binding
11.4.1 NetMHC
11.4.2 NetMHCPan
11.4.3 SYFPEITHI
11.4.4 ProPred-I
11.4.5 RANKPEP
11.4.6 MHCPred
11.4.7 EpiJen
11.4.8 SVMHC
11.4.9 MULTIPRED2
11.4.10 ProPred
11.4.11 MHC2Pred
11.5 CTL Epitope Prediction
11.5.1 NetCTL
11.5.2 CTLPred
11.5.3 NetChop
11.5.4 MAPPP
11.5.5 Pcleavage
11.6 B Cell Epitope Prediction
11.6.1 BCPred
11.6.2 LBtope
11.6.3 ABCPred
11.6.4 BepiPred 2.0
11.6.5 Bcepred
11.6.6 DiscoTope
11.6.7 ElliPro
11.6.8 PEASE
11.7 Methods for In Silico Designing of Epitope-Based Vaccines
11.7.1 Selection of Proteins
11.7.2 Epitope Prediction and Analysis
11.7.3 Molecular Docking and Molecular Dynamics Simulation
11.7.4 Construction of Vaccine
11.8 Case Studies of Vaccine Designing
11.8.1 Vaccine Designing for Viral Pathogens
11.8.2 Vaccine Designing for Bacteria
11.8.3 Vaccine Designing for Other Parasites
11.9 Limitations and Challenges
11.10 Conclusion
References
12: Machine Learning Approaches to Rational Drug Design
12.1 Drug Industry
12.2 Drug Discovery Pipeline
12.2.1 Target Discovery
12.2.2 Target Validation
12.2.3 Lead Identification
12.2.4 Lead Optimization
12.2.5 Preclinical Phase
12.2.6 Clinical Trials
12.2.6.1 Phase I
12.2.6.2 Phase II
12.2.6.3 Phase III
12.2.6.4 Phase IV
12.3 Dimensions and Complexity of the Problem and Role of ML Techniques
12.4 Genetic Algorithms
12.4.1 Working of Genetic Algorithms
12.4.2 Genetic Algorithm Operators
12.4.2.1 Natural Selection Operator
12.4.2.2 Stochastic Sampling with Replacement
12.4.2.3 Stochastic Universal Sampling
12.4.2.4 Crossover/Recombination Operator
12.4.2.5 Mutation Operator
12.5 Artificial Neural Networks
12.5.1 How the Human Brain Works?
12.5.2 A Simple Artificial Neuron
12.5.3 Architecture of ANNs
12.6 Deep Learning(DL)
12.7 Support Vector Machines
12.8 Artificial Intelligence and Drug Discovery
12.9 Applications of ANNs, GAs, and Other ML Algorithms in Drug Discovery
12.9.1 Molecular Docking
12.9.2 Pharmacophore Modeling
12.9.3 Quantitative Structure-Activity Relationship (QSAR)
12.10 Conclusions
References
