About Course
Description
This book provides a comprehensive guide to computer-aided drug design (CADD), covering methodologies such as molecular modeling, virtual screening, and drug optimization. It explores the application of computational tools in drug discovery, offering practical protocols and case studies for researchers. The book is a valuable resource for understanding how computational approaches are transforming pharmaceutical research and development.
Implementation Plan for the Book Club Over Two Months
1. Book Selection
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Book: Computer-Aided Drug Design.
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Level: Beginner to Intermediate .
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Total Chapters: 12 (approximate).
2. Chapter Division
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The book will be divided into 8 parts (one part per week).
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Each week, members will read 1-2 chapters depending on the length and complexity.
3. Weekly Schedule
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Week 1: Chapter 1 (Introduction to Computer-Aided Drug Design) + Chapter 2 (Molecular Modeling Basics).
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Week 2: Chapter 3 (Virtual Screening Techniques) + Chapter 4 (Ligand-Based Drug Design).
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Week 3: Chapter 5 (Structure-Based Drug Design) + Chapter 6 (Molecular Docking).
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Week 4: Chapter 7 (Pharmacophore Modeling) + Chapter 8 (Drug Optimization Strategies).
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Week 5: Chapter 9 (Case Studies in Drug Discovery) + Chapter 10 (Challenges in CADD).
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Week 6: Chapter 11 (Future Directions in CADD) + Chapter 12 (Conclusion and Summary).
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Week 7: Review and Recap of Key Concepts.
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Week 8: Final Discussion and Evaluation.
4. Weekly Meetings
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Duration: 1-2 hours per meeting.
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Agenda:
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Discuss the assigned chapters.
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Explain complex concepts with the help of an instructor.
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Answer members’ questions.
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Open discussion on ideas presented in the chapters.
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Use interactive tools like presentations or videos to enhance understanding.
5. Interactive Activities
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Workshops: Organize practical workshops on using CADD tools (e.g., molecular docking software).
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Side Discussions: Create a Facebook or WhatsApp group for discussions outside meetings.
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Weekly Challenges: For example, writing a summary of the week’s chapters or analyzing a small dataset.
6. Final Evaluation
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At the end of the two months, conduct a final evaluation:
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Survey to assess the reading and meeting experience.
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General discussion session about the book as a whole.
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Members share their personal evaluation of the book and what they learned.
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What Will You Learn?
- Understand the fundamentals of computer-aided drug design (CADD) and its role in drug discovery.
- Learn techniques for molecular modeling, virtual screening, and drug optimization.
- Gain practical skills in using CADD tools and software.
- Explore case studies and real-world applications of CADD in pharmaceutical research.
- Apply computational methods to solve drug discovery challenges.
Course Content
Before You Start: Book Club Orientation
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05:46
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11:35
Computer-Aided Drug Design | Book Club
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Computer-Aided Drug Design | Book Club
chapter 1
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Abstract
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1 Introduction
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1 .2 Structure Based Drug Designing
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1.2.1 Target Identification .mp4
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1 .2. 2 Modeling and Visualization of Macromolecule Structure
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1 .2. 3 Binding Site Prediction and Analysis
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1. 2. 4 Molecular Docking
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1. 2. 4. 1 Flexible Docking
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1. 2 .4. 2 Rigid Docking
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1. 2. 5 Structure Based Virtual Screening
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1. 2. 6 Validation of Molecular Docking
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1. 3 Ligand Based Designing
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1.3.2 Quantitative Structure–Activity Relationship (QSAR)
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1 .4 Computation of HOMO and LUMO Energy
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1. 5 ADMET Prediction and Analysis
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1. 6 Molecular Dynamics Simulation
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1. 7 Identification of New Drug Like Molecules
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1. 8 Discovery and Designing of Natural Lead Compounds for Liver Cancer A Case Study
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1. 9 Examples of Drugs Synthesized Using CADD
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1. 10 SuccessandLimitations
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1 . 11 Conclusion
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Chapter 2
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Molecular Modeling of Proteins Methods
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1 Introduction
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1 .1 Amino Acids
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2. 1. 2 Basic Principles of Protein Structure
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2. 2 Explosion of Protein Related Data
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2. 3 Protein Structure Determination
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2. 3 .1 X Ray Crystallography
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2. 3. 2 NMR Spectroscopy
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2. 3 .3 3D Electron Microscopy
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2. 4 Protein Structure Prediction
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2. 4. 1 Homology or Comparative Modeling
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2 .4. 1. 1 Template Recognition and Initial Alignment
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2. 4. 1. 2 Alignment Correction
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2. 4. 1 .3 Modeling Structurally Conserved Region SCR and Backbone Generation
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2. 4. 1. 4 Loop Modeling
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2. 4. 1 .5 Side Chain Modeling
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2 .4. 1 .6 Model Optimization
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2. 4. 1. 7 Model Validation
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2. 4. 2 Fold Recognition or Threading Method
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2 .4. 3 Ab Initio Methods
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2. 5 Evaluation and Validation of Modeled Structure
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2 .6 Recent Advances in Prediction Approaches
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2. 7 Applications
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2.8 END OF CH2
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Chapter 3
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1 . Abstract
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3 .1 Introduction
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3 .2 Target Molecule
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3. 3 Binding Site and Active Site
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3. 4 Ligand Molecule
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3. 5 Binding Affinity
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3. 6 Chemical Specificity
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3. 7 Binding Site and Molecular Interactions
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3. 7. 1 Protein–Drug Interactions
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3. 7. 2 Drug–Nucleic Acid Interactions
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3. 7 .3 Protein–Protein Interactions
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3 .7. 4 Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate
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3. 8 Binding Site Prediction
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3. 8. 1 Evolutionary AlgorithmsSequence Based Predictions
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3 .8. 2 Energy Based Algorithms
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3. 8 .3 Geometry Based AlgorithmStructure Based Predictions 2
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3 .9 Approaches
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3. 10 Prediction Tools and Servers
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3. 11 Validation of Binding Site
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3. 12 Role of the Binding Site in Drug Designing
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3. 13 Recent Advances and Future Perspective
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END OF CH 3
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Chapter 4
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1. Abstract
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4. 1 Introduction
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4. 1. 1 ADMET Prediction
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4. 1. 2 ADMET Parameters and their Role
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4. 2 Importance of ADMET
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4. 3 The Evolving Science of ADMET
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4. 4 Blood–Brain Barrier Models
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4 .5 ADMET Prediction
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4. 6 Strategies for the Designing of ADMET Model
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4. 6 .3 ADMET Prediction Methods and Tools
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4. 7 ADMET Tools
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4. 8 Challenges in Present Scenario and Future Prospective
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End of ch 4 Role of ADMET Tools in Current Scenario
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Chapter 5
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Abstract
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5. 1 Introduction
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5 .2 Therapeutic Target Information
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5. 2 .1 Universal Protein Resource UniProt
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5. 2. 2 uniport
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5. 2 .2 Protein Data Bank PDB
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5 .2. 3 Molecular Modeling Database
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5. 2. 4 Therapeutic Target Database
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5. 2. 5 Herbal Ingredients Targets HIT Database
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5. 2. 6 SuperTarget
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5. 3 Chemical Information
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5. 3. 1 PubChem
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5. 3. 2 Zinc
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5. 3. 3 ChEMBL
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5. 3. 4 Chemical Entities of Biological Interest ChEBI
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5. 3 .5 NCI Database
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5. 3. 6 ChemDB
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5. 3. 7 ChemSpider
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5 .3 .8 BindingDB
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5. 3 .9 PDBbind
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5. 3 .10 Toxin and Toxin Target Database T3DB
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5. 3. 11 BIAdb
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5 .3. 12 Super Natural II
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5. 3. 13 Naturally Occurring Plant Based Anti Cance
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5. 3 .14 Dictionary of Natural Products Online
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5 .3 .15 Ligand Expo
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5 .3 .16 SuperLigands
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5 .3 .17 Toxicology Data Network
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5 .4 Drug Molecule Information
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5. 4. 1 DrugBank
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5. 4. 2 SuperDRUG2
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5 .4 .3 PharmGKB
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5. 4 .4 Search Tool for Interactions of Chemicals STITCH
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5. 5 Metabolomic Pathway Information
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5. 5. 2 Metabolome Database HMDB
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5 .5. 3 Small Molecule Pathway Database SMPDB
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5 .5. 4 BiGG
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5. 5 .5 MetaboLights Database
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5. 5. 6 BioCyc
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5. 5 .7 Reactome
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5 .5. 8 WikiPathways
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5. 6 Disease and Physiology Information
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5. 6. 1 Online Mendelian Inheritance in Man OMIM
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5 .6 .2 METAGENE
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5. 6. 3 RAMEDIS
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5. 6. 4 OMMBID
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5. 7 Peptide Information
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5. 7 .1 PepBank
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5 .7 .2 StraPep
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5. 7. 3 Antimicrobial Peptide Database APD
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5 .7 .4 CAMPR3
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5. 7. 5 CancerPPD
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5 .8 Challenges and Future Perspective
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END OF CH 5 Database Resources for Drug Discovery
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Chapter 6
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Molecular Docking and Structure Based Drug Design
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6. 1 Introduction
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6 .2 Docking Guidelines
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6. 2. 2 Docking Process
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6. 2. 3 Ligand and Protein Preparation
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6. 2. 4 Ligand Conformations Strategies
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6 .2. 5 Scoring Functions
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6. 2. 5 .1 Force Field
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6. 2. 5. 2 Empirical Scorings
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6. 2. 5. 3 Knowledge Based Scoring
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6. 2. 6 Ensemble Docking
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6. 2. 7 Consensus Docking
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6. 3 Different Types of Docking Based on Interactions
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6. 3. 1 Protein–Ligand Docking
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6. 3. 2 Protein–Peptide like Ligand Docking
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6. 3. 3 Protein–Protein Docking
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6. 4 Water Solvation and Docking
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6. 5 DockingTools
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6. 6 Virtual Screening
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6. 7 Analysis of Docking Results
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6. 8 Limitations of Docking Algorithms and Future Scope
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6. 9 Major Developments in Docking
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END OF CH 6 Molecular Docking and Structure Based Drug
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Chapter 7
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Molecular Dynamics Simulation of Protein
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7 .1 History and Background
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7. 2 Introduction
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7. 3 Principle of MD Simulation
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7. 3. 1 Periodic Boundary Conditions
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7. 3. 2 Ewald Summation Techniques
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7 3 3 Particle Mesh Ewald Method
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7. 3. 4 Thermostats in MD
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7. 3. 5 Solvent Models
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7. 3. 6 Energy Minimization Methods in MD Simulations
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7. 4 Current Tools for MD Simulation
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7. 4. 2 GROMACS
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7. 4. 3 AMBER
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7. 4 .4 CHARMM GUI
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7. 4. 5 NAMD
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7. 4 .6 Quantum MechanicsMolecular Mechanics QMMM
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7. 4 .7 HyperChem
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7 .5 Other Advance Methods for MD Simulation
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7. 6 Analysis of MD Trajectories Through GUI Based Software
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7. 6 .2 PyMOL
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7. 6. 3 Chimera
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7. 7 Structural Parameters for Analysis of MD Simulation
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7 .7. 2 RMSF
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7 .7 .3 RadiusofGyration
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7. 7. 4 Protein–Ligand Contacts
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7 .7. 5 SASA
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7. 7. 6 Principal Component Analysis or Essential Dynamics
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7. 7. 7 Secondary Structure Analysis
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7. 8 Application of MD Simulation
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7. 8. 2 Application in the Drug Designing
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7. 8. 2. 2 Inhibitor Designing Against Fasciola gigantica Thioredoxin Glutathione Reductase
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7 .8 .3 Unfolding Studies
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7. 8. 3. 2 GdnHCl Induced Unfolding Analysis
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7 .8. 3 .3 pH Induced Effects on the Structure and Stability of the Protein
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END OF CH 7 Molecular Dynamics Simulation of Protein
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Chapter 8
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ST OF CH 8 Computational Approaches for Drug Target
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8. 1 Introduction
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8. 2 Drug Targets
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8. 3 Drug Target Identification
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8 .4 Computational Approaches for Drug Target Identification
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8. 5 Homology BasedApproaches
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8. 5 .1 Human Homologs
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8. 5. 2 Human Microbiome Homologs
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8 .5 .3 Essentiality
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8 .5 .4 Virulence Factor Homologs
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8. 5. 5 Drug Target Homologs
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8 .5 .6 Cellular Location
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8. 5. 7 Role in the Biological Pathway
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8 .5 .8 Case Study Subtractive Approach for Drug Target Identification
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8 .6 Network Based Approaches
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8. 6. 2 Limitations
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8. 7 Properties of an Ideal Drug Target
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8. 8 Druggability
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8. 9 Computational Methods for Druggability Assessment
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8 .9 .2 Structure Based Methods
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8. 9 .3 Quantification of Druggability
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8. 9. 4 Major Concern
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8 .10 Target Based Drug Discovery
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8. 11 Summary
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Chapter 9
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ST OF CH9 Computational Screening Techniques for Lead
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9. 1 Introduction
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9 .2 High Throughput Screening
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9. 2, 1 Assay Design
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9. 2 .2 Biochemical Assays
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9. 2. 3 Whole Cell Assays
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9 .2 .4 Automatic Methods of Library Generation and Robotics in HTS
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9. 2. 5 Profiling
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9. 2 .6 Screening Expense and Outsourcing Screen
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9. 3 QSAR Theories
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9. 4 Molecular Descriptors Used in QSAR
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9 .5 Methods of QSAR
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9. 5. 1 2D QSAR Methods
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9. 5. 2 3D QSAR
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9. 5 .3 4D QSAR
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9. 5 .4 5D QSAR
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9. 5. 5 4D vs 5D QSAR
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9. 6 ADME
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9. 6 .1 Absorption
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9. 6. 2 Distribution
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9. 6 .3 Metabolism
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9. 6. 4 Excretion
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9. 7 Toxicological Screening
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9. 7. 1 Acute Systemic Toxicity
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9. 7 .3 Structural Alerts and Rule Based Method
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9 .7. 5 Quantitative Structure Activity Relationship Model Using a Statistical Method
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9 ,8 Limitations and Future Scope
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END OF CH9 Computational Screening Techniques for Lead Des
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Chapter 10
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ST OF CH 10 Advances in Pharmacophore Modeling
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10 .1 Introduction
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10 .2 Features in a Pharmacophore
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10. 3 Pharmacophore Modeling
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10. 3. 2 Building a Pharmacophore
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10 .3 .3 Algorithms Used to Build a Pharmacophore
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10 .3. 4 Structure Based Pharmacophore
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10. 4 Tools for Pharmacophore Building
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10. 5 Validation of a Pharmacophore Hypothesis
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10 .6 A Case Study of Structure and Ligand Based Pharmacophore
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Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate
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10. 7. 2 Pharmacophore Fingerprint
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10. 7. 3 De Novo Ligand Design
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10. 8 Success Stories in Pharmacophore Based Drug Designing
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10. 9 Significance of Pharmacophore
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10 .10 Downside of Pharmacophore Modeling
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10. 11 Conclusion
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Chapter 11
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In Silico Designing of Vaccines Methods, Tools, and Their Limitations ..Abstract
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11. 1 Introduction
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11 .1 .1 Live Attenuated Vaccine
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11. 1 2 Inactivated Vaccine
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11. 1. 3 Subunit Vaccine
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11. 1. 4 Recombinant Vector and DNA Vaccines
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11 .1. 5 Epitope Based Vaccines
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11. 2 Band TCell Epitopes
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11 .3 Bioinformatics in Vaccine Design
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11 .4 Prediction Tools for Class I and II MHC Binding
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11. 5 CTL Epitope Prediction
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11. 5 .1 NetCTL
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11 .7 Methods for In Silico Designing of Epitope Based Vaccines
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11. 7. 1 Selection of Proteins
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11. 7. 2 Epitope Prediction and Analysis
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11. 7. 3 Molecular Docking and Molecular Dynamics Simulation
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11 .7. 4 Construction of Vaccine
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11. 8 Case Studies of Vaccine Designing
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11. 8 .2 Vaccine Designing for Bacteria
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11. 8 .3 Vaccine Designing for Other Parasites
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11. 9 Limitations and Challenges
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11. 10 In Silico Designing of Vaccines Methods, Tools, and Their Limitations
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Chapter 12
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Abstract ….Machine Learning Approaches to Rational Drug Design
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12. 1 Drug Industry
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12. 2 Drug Discovery Pipeline
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12. 3 Complexity of the Problem and Role of ML Techniques
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12. 4 Genetic Algorithms
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12 .4. 2 Genetic Algorithm Operators
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12. 5 Artificial Neural Networks
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12. 6 Deep LearningDL
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12. 7 Support Vector Machines
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12. 8 Artificial Intelligence and Drug Discovery
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12. 9 ANNs, GAs, and Other ML Algorithms inDrug Discovery
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12. 9. 1 Molecular Docking
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12. 9. 2 Pharmacophore Modeling
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12. 9 .3 Quantitative Structure–Activity Relationship QSAR
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10 Conclusions
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