CADD and Informatics in Drug Discovery

Preface
Contents
About the Editors
Chapter 1: Role of Bioinformatics in Drug Design and Discovery
1.1 Introduction
1.2 Genome Sequencing and Genomic Exons Information in Drug Discovery
1.2.1 Genetic Diseases
1.2.2 Human Diseases Caused by Pathogens
1.3 Epigenetics, Genome Architecture, and Cistromes in Drug Discovery
1.4 Transcriptomics and Drug Discovery
1.4.1 Phenotype Screening
1.4.2 Drug Target Identification
1.5 Proteomic Data and Drug Discovery
1.6 Ribosome Profiling and Drug Discovery
1.7 Structural Biology and Drug Discovery
1.8 Bioinformatics and Drug Resistance
1.9 Bioinformatics Software and Database
1.10 Conclusion
References
Chapter 2: Computational Modelling and Simulations in Drug Design
2.1 Introduction
2.2 Computational Modelling and Methods in Drug Discovery
2.2.1 Structure-Based CADD
2.2.2 Ligand-Based CADD
2.3 ADMET Prediction in CADD
2.4 Molecular Dynamics Simulation in Drug Discovery
2.4.1 Process of MD Simulation
2.4.2 Software Used in MD Simulation
2.5 Case Studies
2.6 Conclusion and Future Perspectives
References
Chapter 3: Informatics: Tools and Databases in Drug Discovery
3.1 Introduction
3.1.1 Databases
3.1.2 Database Structure
3.1.3 Database Management System (DBMS)
3.1.4 Bioinformatics
3.1.5 Cheminformatics
3.2 Drug Discovery Informatics
3.2.1 Information Resources
3.2.1.1 Literature Databases
3.2.1.2 Chemical Databases
3.2.1.3 Biological Databases
Sequence Databases
Structure Databases
3.2.2 Software Tools Used in Drug Discovery
3.2.2.1 Chemical Drawing Tools
3.2.2.2 Molecular Modeling Tools
3.2.2.3 Homology Modeling Tools
3.2.2.4 Binding Site Prediction Tools
3.2.2.5 Docking Tools
3.2.2.6 Pharmacophore Finding Tools
3.2.2.7 Screening Tools
3.2.2.8 Target Prediction Tools
3.2.2.9 Ligand Design Tools
3.2.2.10 Binding-Free Energy Estimation
3.2.2.11 QSAR Prediction Tools
3.2.2.12 ADME Toxicity Prediction Tools
3.3 Conclusion and Future Perspectives
References
Chapter 4: Multi-Omics Approaches in Drug Discovery
4.1 Introduction
4.1.1 Definition of Multi-Omics
4.1.2 Different Omic Strategies of Multi-Omics Studies
4.2 Omics Data Types and Repositories
4.2.1 Genomics
4.2.2 Epigenomics
4.2.3 Transcriptomics
4.2.4 Proteomics
4.2.5 Metabolomics
4.3 Multi-Omics Data Repositories
4.4 Strategies Toward Multi-Omics Studies
4.4.1 Design of Omics Studies
4.4.1.1 Complexity of Diseases
4.4.1.2 Power, Sample Sizes, and Subsequent Analysis
4.4.1.3 Human Study and Animal Model of Disease
4.4.2 Analysis and Network Methods
4.4.3 Leveraging Multi-Omics Data for Actionable Insights
4.5 Multi-Omics Approaches in Drug Discovery
4.5.1 Target Identification
4.5.2 Mechanism of Action and Cell Systems Biology
4.5.3 Phenotypic Drug Discovery
4.6 Application of Multi-Omics Technologies in Tubercular Drug Discovery
4.6.1 Genomics
4.6.1.1 Target Identification
4.6.1.2 Mode of Action
4.6.2 Transcriptomics
4.6.2.1 Target Identification
4.6.2.2 Mode of Action
4.6.3 Proteomics
4.6.3.1 Target Identification
4.6.3.2 Mode of Action
4.6.4 Metabolomics
4.6.4.1 Target Identification
4.6.4.2 Mode of Action
4.6.5 Lipidomics
4.6.5.1 Target Identification
4.6.5.2 Mode of Action
4.7 Conclusion and Future Perspectives
References
Chapter 5: Computational Methods in Natural Products-Based Drug Discovery
5.1 Introduction
5.2 Natural Products´ Collections
5.2.1 Physical Collection
5.2.2 Virtual Collection
5.3 Cheminformatics and Computational Approaches for NP-Based Drug Discovery
5.3.1 Computational-Based Approaches
5.3.2 Cheminformatics and NP-Based Pipeline
5.4 Computational Approaches Related to Natural Products
5.4.1 Structural Elucidation
5.4.2 Analysis of Physicochemical and Structural Properties
5.4.3 Structural Diversity Analysis
5.4.4 Natural Product-Likeness Assessment
5.4.5 Identification of Bioactive Natural Products
5.4.6 Determination of Macromolecular Targets
5.4.7 Prediction of ADME and Safety Profiles of NPs
5.4.8 Case Study
5.5 Challenges to Computational Approaches
5.6 Conclusion and Future Perspectives
References
Chapter 6: Virtual Screening in Lead Discovery
6.1 Introduction
6.2 Virtual Screening Methods
6.2.1 Structure-Based Virtual Screening
6.2.2 Ligand-Based Virtual Screening
6.2.3 Pharmacophore-Based Virtual Screening
6.2.4 Receptor Structure-Based Methods
6.2.5 Counting Methods
6.3 Functional Group Filters
6.4 Docking
6.5 Other Tools
6.5.1 Fragment-Based Virtual Screening (FBVS)
6.5.2 Text-Mining Techniques
6.5.3 Other Techniques
6.6 Role of Virtual Screening in Drug Discovery
6.6.1 Case Study 1
6.6.2 Case Study 2
6.7 Conclusion
References
Chapter 7: Target-Based Screening for Lead Discovery
7.1 Introduction
7.1.1 Target-Based Drug Discovery Strategy
7.1.2 Types of Drug Discovery Approaches
7.1.2.1 High-Throughput Screening and High-Content Screening
7.1.2.2 Fragment-Based Drug Discovery
7.1.2.3 Virtual Screening
7.2 Structure-Based Drug Designing (SBDD)
7.2.1 Homology Modeling
7.2.2 Rational Drug Design (Role of SBDD)
7.2.3 Approaches of SBDD
7.2.3.1 Ligand-Based Drug Design
Quantitative Structure-Activity Relationship (QSAR) Models
Pharmacophore Modeling
7.2.3.2 Receptor-Based Drug Designing
Docking
7.3 Advantages, Applications, and Challenges
7.3.1 Advantages of Target-Based Approaches
7.3.2 Challenges of Target-Based Approaches
7.3.3 Promising Examples of TBDD
7.3.4 Molecular Docking
7.3.4.1 Molecular Docking Approaches
Shape Complementarity Approach
Simulation Approach
7.3.4.2 Docking-Its Mechanisms
7.3.5 Molecular Dynamics
7.4 Target-Based Screening Versus Phenotypic Screening
7.4.1 What Does Each Screening Approach Involve?
7.4.2 Combination Approaches
7.4.3 How Can These Screenings Be Taken to the Next Level?
7.5 Case Studies and Examples
7.5.1 Case Study 1
7.6 Future Roadmap
7.7 Learning Outcomes
References
Chapter 8: Fragment-Based Drug Design in Lead Discovery
8.1 Introduction
8.2 Fragment Finding
8.2.1 Library Building
8.2.2 Protein Hot Spots Identification
8.2.3 Computational Prediction
8.3 Experimental Identification of Fragments
8.3.1 Nuclear Magnetic Resonance
8.3.2 Surface Plasmon Resonance
8.3.3 X-Ray Crystallography
8.3.4 Thermal Shift Assay
8.3.5 Isothermal Titration Calorimetry
8.3.6 Mass Spectrometry
8.4 FBDD Strategies
8.4.1 Chemical Biology Exploration of Biological Targets
8.4.2 FBDD as HTS Complimentary
8.4.3 Build-Up Core FBDD for Drug Discovery
8.5 Case Studies
8.5.1 Case 1: Pseudo-Natural Products
8.5.2 Case 2: SARS-CoV-2 Main Protease Inhibitors
8.5.3 Case 3: p38 Mitogen-Activated Protein Kinases
8.6 Conclusion and Future Perspectives
References
Chapter 9: Artificial Intelligence and Machine Learning in Drug Discovery
9.1 Introduction
9.2 Artificial Intelligence
9.2.1 Concept of Modernization
9.2.2 Models
9.2.2.1 AI-Guided Target Identification
9.2.2.2 Network-Based Approaches
9.2.2.3 Machine Learning-Based Approaches
9.2.2.4 AI-Guided Hit Identification
9.2.2.5 Structure-Based Approaches
9.2.2.6 Ligand-Based Approaches
9.2.2.7 Chemogenomic Approaches
9.2.2.8 AI-Guided ADMET Prediction
9.2.2.9 AI-Guided Lead Optimization
9.3 Applications
9.3.1 Disease Prediction and Diagnosis
9.3.2 Clinical Trials and In Silico-Based Prediction
9.3.3 Drug Discovery and Repurposing
9.4 Machine Learning
9.4.1 Classifications
9.4.2 ML Algorithms Used in Drug Discovery
9.4.2.1 Naive Bayes
9.4.2.2 Naive Bayes in Drug Discovery
9.4.2.3 Support Vector Machines
9.4.2.4 Support Vector Machines in Drug Discovery
9.4.2.5 Tree-Based Models
9.4.2.6 Random Forest in Drug Discovery
9.4.2.7 Artificial Neural Networks
9.4.2.8 ANN in Drug Discovery
9.4.2.9 De novo Molecular Design
9.4.2.10 Synthesis Planning
9.5 Applications
9.5.1 CNS Disorder
9.5.2 Discovering Novel Antimicrobial Agents
9.5.3 Epidemic COVID
9.6 Drug Discovery Process
9.6.1 AI and Machine Learning in Precision Drug Discovery
9.6.1.1 NGS and Molecular Profiling
9.6.1.2 Biomarkers
9.6.1.3 Medical Imaging
9.6.1.4 Radiographic Imaging
9.6.2 Repurposed Drug/Drug Discovery by AI/ML Approach
9.7 Limitations of AI/ML Approaches
9.8 Conclusions and Future Perspectives
References
Chapter 10: Network Pharmacology and Systems Biology in Drug Discovery
10.1 Introduction
10.1.1 Drug Discovery and Development Process
10.1.2 Role of Computational Methods in Drug Discovery
10.1.3 Concept and Significance of Network Pharmacology
10.1.4 Systems Biology
10.2 Network Pharmacology: Practical Guide
10.2.1 Common Network Pharmacology Databases
10.2.2 Research Approaches of Network Pharmacology
10.2.3 Data Collection and Validation
10.2.4 Network Analysis and Visualization
10.3 Applications of Network Pharmacology in Drug Discovery
10.3.1 Applications of Network Pharmacology for Plant-Based Drug Discovery
10.3.1.1 Case Study I: Network Pharmacology-Based Virtual Screening of Active Constituents of Prunella vulgaris L. Against Bre...
10.3.1.2 Case Study II: A Network Pharmacology Approach to Investigate the Anticancer Mechanism and Potential Active Ingredien...
10.3.1.3 Case Study III: Network Pharmacology-Based Virtual Screening of Active Constituents of Cnidium monnieri in Treating H...
10.3.2 Applications of Network Pharmacology for Phytoconstituents
10.3.2.1 Case Study I: Network Pharmacology-Based Virtual Screening of Resveratrol Which Can Alleviate (Xiao et al. 2021)
10.3.2.2 Case Study II: Network Pharmacology-Based Virtual Screening of Curcumin against Triple-Negative Breast Cancer (TNBC) ...
10.4 Computational Approaches in System Biology
10.4.1 Top-Down Approach
10.4.2 Bottom-up Approach
10.4.3 System Biology Application in Drug Discovery
10.4.3.1 Target Identification
10.4.3.2 Mechanism of Action
10.4.3.3 Biomarkers Identification
10.5 Conclusion and Future Prospects
References
Chapter 11: In Silico Pharmacology and Drug Repurposing Approaches
11.1 Introduction
11.1.1 Introduction to Drug Repurposing
11.1.2 Conventional/Current Drug Discovery Process Vs. Drug Repurposing, an Old Weapon for New Battle?
11.1.3 Fundamentals of Drug Repurposing
11.1.4 Advantages of Drug Repurposing Over Typical Drug Development Process
11.2 Drug Repurposing Strategies
11.2.1 Knowledge-Based Repurposing
11.2.2 Target-Based Drug Repurposing
11.2.3 Pathway-Based Drug Repurposing
11.2.4 Target Mechanism-Based Drug Repurposing
11.2.5 Signature-Based Repurposing
11.2.6 Phenotype-Based Repurposing
11.3 Methods for Computational Drug Repurposing
11.3.1 Machine Learning
11.3.2 Network Models
11.3.3 Text Mining and Semantic Inference
11.4 Validation for Computational Repurposing
11.5 Success Stories of Drug Repurposing
11.5.1 Pimozide (Antipsychotic Drug)
11.5.2 Valproate (Antiepileptic Drugs)
11.5.3 Amiodarone (Antiarrhythmic Drug)
11.5.4 Sildenafi
11.5.5 Pertuzumab
11.5.6 Thalidomide
11.5.7 Repurposing in Malaria
11.5.7.1 Dapsone
11.5.7.2 Drug Repurposing in COVID-19
11.6 Opportunities and Limitations of In Silico Drug Repurposing
11.7 Conclusion
References
Chapter 12: CADD Approaches in Anticancer Drug Discovery
12.1 Introduction
12.1.1 Structure-Based Computer-Aided Drug Design (SB-CADD) Approach
12.1.2 Ligand-Based Computer-Aided Drug Design (LB-CADD) Approach
12.2 Computational Approaches for Anticancer Drug Discovery
12.2.1 Anticancer Small Organic Molecules Design
12.2.2 Anticancer Peptide Design
12.2.3 QSAR Modeling
12.2.4 Pharmacophore Mapping
12.2.5 Simulation of Molecular Docking and Molecular Dynamics of Small Molecules
12.2.6 Discovery of New Binding Sites Aided by Molecular Dynamics
12.3 Recent Advances in Computational Approaches for Anticancer Drug Discovery
12.3.1 Use of Machine Learning (ML) Algorithms
12.3.2 Drug Repurposing (DR) for Anticancer Drug Discovery
12.3.3 Pseudoreceptor Modeling
12.3.4 Proteochemometric Modeling
12.4 Conclusion and Future Perspectives
References
Chapter 13: CADD Approaches and Antiviral Drug Discovery
13.1 Introduction
13.2 Methodology for CADD
13.2.1 Structure-Based Drug Design (SBDD)
13.2.2 Ligand-Based Drug Design (LBDD)
13.2.3 Machine Learning (ML)
13.2.4 Deep Learning (DL)
13.2.5 Virtual Screening (VS)
13.3 Host and Viral Proteins as Target
13.3.1 Chemokine Receptors
13.3.2 Viral Chemokine Receptors
13.3.3 Viral Chemokine Ligand
13.3.4 Viral CKBPs
13.3.5 Glycoproteins
13.3.6 Glycoprotein of Virus
13.3.7 Glycoprotein of Host
13.3.8 Kinases
13.3.9 Lipid Kinase
13.3.10 Numb-Associated Kinases (NAKs)
13.3.11 Receptor Tyrosine Kinases (RTKs)
13.3.12 Mitogen-Activated Protein Kinases (MAPKs)
13.3.13 Src Kinases
13.3.14 Cyclin-Dependent Kinases (CDKs)
13.3.15 Other Proteins
13.3.16 Cytoskeleton Protein (Actin)
13.3.17 Annexins
13.4 Conclusion and Future Perspectives
References
Chapter 14: CADD Approaches in Anti-inflammatory Drug Discovery
14.1 Introduction
14.2 Drug Development Strategy and CADD Ideas
14.3 Inflammation and Its Mechanisms
14.3.1 Mechanism of Inflammation (Muzamil et al. 2021)
14.3.2 Molecular Process of Inflammation
14.4 Anti-inflammatory Drugs and Their Classification and Mechanism of Action (MOA)
14.5 Anti-inflammatory Drug Discovery Using CADD
14.6 Anti-inflammatory Drugs Discovered Using CADD Approaches: An Update
14.7 Case Study (Omar et al. 2018)
14.7.1 Design
14.7.2 Chemistry
14.7.3 Biology
14.7.4 Molecular Modeling
14.8 Conclusion and Future Scope
References
Chapter 15: Drug Repurposing and Computational Drug Discovery for Viral Infections and COVID-19
15.1 Introduction
15.2 Viral Infections and COVID-19
15.3 Drug Repurposing
15.3.1 Drug Repurposing for COVID-19
15.3.1.1 Favipiravir
15.3.1.2 Remdesivir
15.3.1.3 Ribavirin
15.3.1.4 Darunavir
15.3.1.5 Ritonavir
15.3.1.6 Arbidol
15.3.1.7 Chloroquine and Hydroxychloroquine
15.3.1.8 Tocilizumab
15.3.1.9 Oseltamivir
15.3.1.10 REGN-COV2
15.4 Computational Methods
15.4.1 Molecular Docking Methods
15.4.2 Network-Based Techniques
15.4.3 Connectivity-MAP (CMAP) Methods
15.4.4 Data for Specific Goal
15.5 Summary and Conclusion
References