Computer-Aided Drug Design | Book Club |
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
06:09 -
1 Introduction
07:22 -
1 .2 Structure Based Drug Designing
04:40 -
1.2.1 Target Identification .mp4
05:44 -
1 .2. 2 Modeling and Visualization of Macromolecule Structure
05:56 -
1 .2. 3 Binding Site Prediction and Analysis
07:30 -
1. 2. 4 Molecular Docking
04:16 -
1. 2. 4. 1 Flexible Docking
04:58 -
1. 2 .4. 2 Rigid Docking
04:52 -
1. 2. 5 Structure Based Virtual Screening
04:30 -
1. 2. 6 Validation of Molecular Docking
06:16 -
1. 3 Ligand Based Designing
05:47 -
1.3.2 Quantitative Structure–Activity Relationship (QSAR)
06:35 -
1 .4 Computation of HOMO and LUMO Energy
06:12 -
1. 5 ADMET Prediction and Analysis
06:05 -
1. 6 Molecular Dynamics Simulation
05:48 -
1. 7 Identification of New Drug Like Molecules
05:39 -
1. 8 Discovery and Designing of Natural Lead Compounds for Liver Cancer A Case Study
06:18 -
1. 9 Examples of Drugs Synthesized Using CADD
04:07 -
1. 10 SuccessandLimitations
05:31 -
1 . 11 Conclusion
03:18
Chapter 2
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Molecular Modeling of Proteins Methods
05:09 -
1 Introduction
05:04 -
1 .1 Amino Acids
05:54 -
2. 1. 2 Basic Principles of Protein Structure
05:45 -
2. 2 Explosion of Protein Related Data
04:58 -
2. 3 Protein Structure Determination
04:16 -
2. 3 .1 X Ray Crystallography
04:54 -
2. 3. 2 NMR Spectroscopy
05:24 -
2. 3 .3 3D Electron Microscopy
05:21 -
2. 4 Protein Structure Prediction
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2. 4. 1 Homology or Comparative Modeling
05:51 -
2 .4. 1. 1 Template Recognition and Initial Alignment
05:42 -
2. 4. 1. 2 Alignment Correction
08:24 -
2. 4. 1 .3 Modeling Structurally Conserved Region SCR and Backbone Generation
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2. 4. 1. 4 Loop Modeling
06:06 -
2. 4. 1 .5 Side Chain Modeling
04:47 -
2 .4. 1 .6 Model Optimization
06:03 -
2. 4. 1. 7 Model Validation
06:26 -
2. 4. 2 Fold Recognition or Threading Method
07:26 -
2 .4. 3 Ab Initio Methods
07:03 -
2. 5 Evaluation and Validation of Modeled Structure
07:56 -
2 .6 Recent Advances in Prediction Approaches
03:13 -
2. 7 Applications
06:16 -
2.8 END OF CH2
03:52
Chapter 3
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1 . Abstract
06:56 -
3 .1 Introduction
05:46 -
3 .2 Target Molecule
04:46 -
3. 3 Binding Site and Active Site
06:16 -
3. 4 Ligand Molecule
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3. 5 Binding Affinity
05:55 -
3. 6 Chemical Specificity
04:09 -
3. 7 Binding Site and Molecular Interactions
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3. 7. 1 Protein–Drug Interactions
07:06 -
3. 7. 2 Drug–Nucleic Acid Interactions
05:25 -
3. 7 .3 Protein–Protein Interactions
06:14 -
3 .7. 4 Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate
04:59 -
3. 8 Binding Site Prediction
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3. 8. 1 Evolutionary AlgorithmsSequence Based Predictions
05:30 -
3 .8. 2 Energy Based Algorithms
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3. 8 .3 Geometry Based AlgorithmStructure Based Predictions 2
06:25 -
3 .9 Approaches
06:30 -
3. 10 Prediction Tools and Servers
06:15 -
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
04:05
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
07:58 -
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
07:42 -
4. 6 Strategies for the Designing of ADMET Model
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4. 6 .3 ADMET Prediction Methods and Tools
07:54 -
4. 7 ADMET Tools
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4. 8 Challenges in Present Scenario and Future Prospective
06:18 -
End of ch 4 Role of ADMET Tools in Current Scenario
03:45
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
03:16
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
06:31 -
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
05:48 -
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
00:00
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
04:49 -
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
05:30 -
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
06:04 -
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
04:07
Chapter 8
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ST OF CH 8 Computational Approaches for Drug Target
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8. 1 Introduction
04:51 -
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
04:00 -
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
05:44 -
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
05:08 -
9. 1 Introduction
06:52 -
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
05:29 -
9. 2. 5 Profiling
05:58 -
9. 2 .6 Screening Expense and Outsourcing Screen
04:49 -
9. 3 QSAR Theories
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9. 4 Molecular Descriptors Used in QSAR
05:59 -
9 .5 Methods of QSAR
04:54 -
9. 5. 1 2D QSAR Methods
05:38 -
9. 5. 2 3D QSAR
07:31 -
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
05:33 -
9. 6 .1 Absorption
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9. 6. 2 Distribution
06:34 -
9. 6 .3 Metabolism
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9. 6. 4 Excretion
06:01 -
9. 7 Toxicological Screening
06:40 -
9. 7. 1 Acute Systemic Toxicity
08:20 -
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
00:00
Chapter 10
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ST OF CH 10 Advances in Pharmacophore Modeling
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10 .1 Introduction
05:31 -
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
06:10 -
10 .6 A Case Study of Structure and Ligand Based Pharmacophore
06:21 -
Interaction of Protein with Nucleic Acid, Lipid, and Carbohydrate
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10. 7. 2 Pharmacophore Fingerprint
07:03 -
10. 7. 3 De Novo Ligand Design
00:00 -
10. 8 Success Stories in Pharmacophore Based Drug Designing
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10. 9 Significance of Pharmacophore
05:57 -
10 .10 Downside of Pharmacophore Modeling
06:13 -
10. 11 Conclusion
00:00
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
04:47 -
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
00:00 -
11 .4 Prediction Tools for Class I and II MHC Binding
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11. 5 CTL Epitope Prediction
08:15 -
11. 5 .1 NetCTL
00:00 -
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
00:00 -
11. 7. 3 Molecular Docking and Molecular Dynamics Simulation
06:23 -
11 .7. 4 Construction of Vaccine
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11. 8 Case Studies of Vaccine Designing
07:21 -
11. 8 .2 Vaccine Designing for Bacteria
07:39 -
11. 8 .3 Vaccine Designing for Other Parasites
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11. 9 Limitations and Challenges
08:04 -
11. 10 In Silico Designing of Vaccines Methods, Tools, and Their Limitations
00:00
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
06:19 -
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
06:00 -
12. 9 ANNs, GAs, and Other ML Algorithms inDrug Discovery
06:33 -
12. 9. 1 Molecular Docking
04:36 -
12. 9. 2 Pharmacophore Modeling
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12. 9 .3 Quantitative Structure–Activity Relationship QSAR
05:38 -
10 Conclusions
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