Computer-Aided Drug Design | Book Club |
Categories: Book-Club, Computer-Aided Drug Design (CADD)
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
04:39 -
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
06:17 -
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
03:17
Chapter 2
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Molecular Modeling of Proteins Methods
05:09 -
1 Introduction
05:04 -
1 .1 Amino Acids
05:55 -
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
05:20 -
2. 4 Protein Structure Prediction
06:06 -
2. 4. 1 Homology or Comparative Modeling
05:50 -
2 .4. 1. 1 Template Recognition and Initial Alignment
05:41 -
2. 4. 1. 2 Alignment Correction
08:24 -
2. 4. 1 .3 Modeling Structurally Conserved Region SCR and Backbone Generation
05:51 -
2. 4. 1. 4 Loop Modeling
06:06 -
2. 4. 1 .5 Side Chain Modeling
04:47 -
2 .4. 1 .6 Model Optimization
06:02 -
2. 4. 1. 7 Model Validation
06:26 -
2. 4. 2 Fold Recognition or Threading Method
07:25 -
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
06:15 -
2.8 END OF CH2
03:51
Chapter 3
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1 . Abstract
06:56 -
3 .1 Introduction
05:45 -
3 .2 Target Molecule
04:46 -
3. 3 Binding Site and Active Site
06:16 -
3. 4 Ligand Molecule
04:55 -
3. 5 Binding Affinity
05:54 -
3. 6 Chemical Specificity
04:09 -
3. 7 Binding Site and Molecular Interactions
05:03 -
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
05:49 -
3. 8. 1 Evolutionary AlgorithmsSequence Based Predictions
05:30 -
3 .8. 2 Energy Based Algorithms
05:24 -
3. 8 .3 Geometry Based AlgorithmStructure Based Predictions 2
06:25 -
3 .9 Approaches
06:30 -
3. 10 Prediction Tools and Servers
06:14 -
3. 11 Validation of Binding Site
06:18 -
3. 12 Role of the Binding Site in Drug Designing
05:11 -
3. 13 Recent Advances and Future Perspective
05:02 -
END OF CH 3
04:04
Chapter 4
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1. Abstract
06:30 -
4. 1 Introduction
05:37 -
4. 1. 1 ADMET Prediction
04:19 -
4. 1. 2 ADMET Parameters and their Role
07:57 -
4. 2 Importance of ADMET
05:45 -
4. 3 The Evolving Science of ADMET
06:15 -
4. 4 Blood–Brain Barrier Models
06:07 -
4 .5 ADMET Prediction
07:42 -
4. 6 Strategies for the Designing of ADMET Model
05:34 -
4. 6 .3 ADMET Prediction Methods and Tools
07:54 -
4. 7 ADMET Tools
06:04 -
4. 8 Challenges in Present Scenario and Future Prospective
06:17 -
End of ch 4 Role of ADMET Tools in Current Scenario
03:45
Chapter 5
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Abstract
06:16 -
5. 1 Introduction
06:25 -
5 .2 Therapeutic Target Information
04:07 -
5. 2 .1 Universal Protein Resource UniProt
06:33 -
5. 2. 2 uniport
06:33 -
5. 2 .2 Protein Data Bank PDB
04:46 -
5 .2. 3 Molecular Modeling Database
06:25 -
5. 2. 4 Therapeutic Target Database
05:29 -
5. 2. 5 Herbal Ingredients Targets HIT Database
06:08 -
5. 2. 6 SuperTarget
04:29 -
5. 3 Chemical Information
04:49 -
5. 3. 1 PubChem
04:52 -
5. 3. 2 Zinc
03:46 -
5. 3. 3 ChEMBL
07:28 -
5. 3. 4 Chemical Entities of Biological Interest ChEBI
04:52 -
5. 3 .5 NCI Database
04:38 -
5. 3. 6 ChemDB
05:16 -
5. 3. 7 ChemSpider
04:09 -
5 .3 .8 BindingDB
05:07 -
5. 3 .9 PDBbind
04:49 -
5. 3 .10 Toxin and Toxin Target Database T3DB
05:55 -
5. 3. 11 BIAdb
04:08 -
5 .3. 12 Super Natural II
04:41 -
5. 3. 13 Naturally Occurring Plant Based Anti Cance
05:44 -
5. 3 .14 Dictionary of Natural Products Online
05:19 -
5 .3 .15 Ligand Expo
05:48 -
5 .3 .16 SuperLigands
04:16 -
5 .3 .17 Toxicology Data Network
04:47 -
5 .4 Drug Molecule Information
04:41 -
5. 4. 1 DrugBank
04:42 -
5. 4. 2 SuperDRUG2
06:32 -
5 .4 .3 PharmGKB
05:00 -
5. 4 .4 Search Tool for Interactions of Chemicals STITCH
05:18 -
5. 5 Metabolomic Pathway Information
04:44 -
5. 5. 2 Metabolome Database HMDB
05:17 -
5 .5. 3 Small Molecule Pathway Database SMPDB
06:29 -
5 .5. 4 BiGG
04:20 -
5. 5 .5 MetaboLights Database
05:58 -
5. 5. 6 BioCyc
04:59 -
5. 5 .7 Reactome
04:53 -
5 .5. 8 WikiPathways
04:45 -
5. 6 Disease and Physiology Information
04:17 -
5. 6. 1 Online Mendelian Inheritance in Man OMIM
04:44 -
5 .6 .2 METAGENE
06:21 -
5. 6. 3 RAMEDIS
05:12 -
5. 6. 4 OMMBID
03:34 -
5. 7 Peptide Information
06:54 -
5. 7 .1 PepBank
04:19 -
5 .7 .2 StraPep
04:41 -
5. 7. 3 Antimicrobial Peptide Database APD
05:01 -
5 .7 .4 CAMPR3
04:18 -
5. 7. 5 CancerPPD
05:38 -
5 .8 Challenges and Future Perspective
05:31 -
END OF CH 5 Database Resources for Drug Discovery
03:15
Chapter 6
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Molecular Docking and Structure Based Drug Design
00:00 -
6. 1 Introduction
07:27 -
6 .2 Docking Guidelines
05:30 -
6. 2. 2 Docking Process
00:00 -
6. 2. 3 Ligand and Protein Preparation
00:00 -
6. 2. 4 Ligand Conformations Strategies
06:35 -
6 .2. 5 Scoring Functions
06:22 -
6. 2. 5 .1 Force Field
05:41 -
6. 2. 5. 2 Empirical Scorings
05:23 -
6. 2. 5. 3 Knowledge Based Scoring
06:31 -
6. 2. 6 Ensemble Docking
04:53 -
6. 2. 7 Consensus Docking
04:54 -
6. 3 Different Types of Docking Based on Interactions
04:37 -
6. 3. 1 Protein–Ligand Docking
05:39 -
6. 3. 2 Protein–Peptide like Ligand Docking
04:45 -
6. 3. 3 Protein–Protein Docking
05:29 -
6. 4 Water Solvation and Docking
05:17 -
6. 5 DockingTools
04:51 -
6. 6 Virtual Screening
05:47 -
6. 7 Analysis of Docking Results
05:10 -
6. 8 Limitations of Docking Algorithms and Future Scope
05:14 -
6. 9 Major Developments in Docking
05:11 -
END OF CH 6 Molecular Docking and Structure Based Drug
06:21
Chapter 7
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Molecular Dynamics Simulation of Protein
07:49 -
7 .1 History and Background
06:06 -
7. 2 Introduction
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7. 3 Principle of MD Simulation
05:29 -
7. 3. 1 Periodic Boundary Conditions
05:17 -
7. 3. 2 Ewald Summation Techniques
05:11 -
7 3 3 Particle Mesh Ewald Method
05:21 -
7. 3. 4 Thermostats in MD
04:49 -
7. 3. 5 Solvent Models
05:35 -
7. 3. 6 Energy Minimization Methods in MD Simulations
05:23 -
7. 4 Current Tools for MD Simulation
05:53 -
7. 4. 2 GROMACS
06:57 -
7. 4. 3 AMBER
05:59 -
7. 4 .4 CHARMM GUI
06:46 -
7. 4. 5 NAMD
05:16 -
7. 4 .6 Quantum MechanicsMolecular Mechanics QMMM
05:33 -
7. 4 .7 HyperChem
05:25 -
7 .5 Other Advance Methods for MD Simulation
06:32 -
7. 6 Analysis of MD Trajectories Through GUI Based Software
06:02 -
7. 6 .2 PyMOL
05:01 -
7. 6. 3 Chimera
05:30 -
7. 7 Structural Parameters for Analysis of MD Simulation
04:53 -
7 .7. 2 RMSF
04:05 -
7 .7 .3 RadiusofGyration
04:04 -
7. 7. 4 Protein–Ligand Contacts
04:37 -
7 .7. 5 SASA
04:57 -
7. 7. 6 Principal Component Analysis or Essential Dynamics
05:17 -
7. 7. 7 Secondary Structure Analysis
04:26 -
7. 8 Application of MD Simulation
07:19 -
7. 8. 2 Application in the Drug Designing
06:03 -
7. 8. 2. 2 Inhibitor Designing Against Fasciola gigantica Thioredoxin Glutathione Reductase
06:37 -
7 .8 .3 Unfolding Studies
06:02 -
7. 8. 3. 2 GdnHCl Induced Unfolding Analysis
06:29 -
7 .8. 3 .3 pH Induced Effects on the Structure and Stability of the Protein
04:22 -
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
05:39 -
8. 1 Introduction
04:50 -
8. 2 Drug Targets
05:57 -
8. 3 Drug Target Identification
06:43 -
8 .4 Computational Approaches for Drug Target Identification
04:57 -
8. 5 Homology BasedApproaches
06:24 -
8. 5 .1 Human Homologs
05:27 -
8. 5. 2 Human Microbiome Homologs
06:26 -
8 .5 .3 Essentiality
03:53 -
8 .5 .4 Virulence Factor Homologs
04:28 -
8. 5. 5 Drug Target Homologs
04:37 -
8 .5 .6 Cellular Location
03:19 -
8. 5. 7 Role in the Biological Pathway
04:00 -
8 .5 .8 Case Study Subtractive Approach for Drug Target Identification
05:12 -
8 .6 Network Based Approaches
00:00 -
8. 6. 2 Limitations
05:43 -
8. 7 Properties of an Ideal Drug Target
04:46 -
8. 8 Druggability
05:18 -
8. 9 Computational Methods for Druggability Assessment
04:11 -
8 .9 .2 Structure Based Methods
07:15 -
8. 9 .3 Quantification of Druggability
05:58 -
8. 9. 4 Major Concern
05:51 -
8 .10 Target Based Drug Discovery
06:57 -
8. 11 Summary
04:16
Chapter 9
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ST OF CH9 Computational Screening Techniques for Lead
05:07 -
9. 1 Introduction
06:52 -
9 .2 High Throughput Screening
06:40 -
9. 2, 1 Assay Design
06:38 -
9. 2 .2 Biochemical Assays
05:48 -
9. 2. 3 Whole Cell Assays
05:46 -
9 .2 .4 Automatic Methods of Library Generation and Robotics in HTS
05:28 -
9. 2. 5 Profiling
05:57 -
9. 2 .6 Screening Expense and Outsourcing Screen
04:48 -
9. 3 QSAR Theories
05:23 -
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
05:04 -
9. 5 .4 5D QSAR
04:15 -
9. 5. 5 4D vs 5D QSAR
05:39 -
9. 6 ADME
05:33 -
9. 6 .1 Absorption
06:50 -
9. 6. 2 Distribution
06:33 -
9. 6 .3 Metabolism
07:01 -
9. 6. 4 Excretion
06:00 -
9. 7 Toxicological Screening
06:39 -
9. 7. 1 Acute Systemic Toxicity
08:19 -
9. 7 .3 Structural Alerts and Rule Based Method
05:24 -
9 .7. 5 Quantitative Structure Activity Relationship Model Using a Statistical Method
07:20 -
9 ,8 Limitations and Future Scope
04:49 -
END OF CH9 Computational Screening Techniques for Lead Des
04:59
Chapter 10
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ST OF CH 10 Advances in Pharmacophore Modeling
05:35 -
10 .1 Introduction
05:31 -
10 .2 Features in a Pharmacophore
05:00 -
10. 3 Pharmacophore Modeling
04:47 -
10. 3. 2 Building a Pharmacophore
06:21 -
10 .3 .3 Algorithms Used to Build a Pharmacophore
06:15 -
10 .3. 4 Structure Based Pharmacophore
06:00 -
10. 4 Tools for Pharmacophore Building
04:35 -
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
04:59 -
10. 7. 2 Pharmacophore Fingerprint
07:03 -
10. 7. 3 De Novo Ligand Design
05:16 -
10. 8 Success Stories in Pharmacophore Based Drug Designing
06:41 -
10. 9 Significance of Pharmacophore
05:57 -
10 .10 Downside of Pharmacophore Modeling
06:12 -
10. 11 Conclusion
04:03
Chapter 11
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In Silico Designing of Vaccines Methods, Tools, and Their Limitations ..Abstract
04:36 -
11. 1 Introduction
06:28 -
11 .1 .1 Live Attenuated Vaccine
05:01 -
11. 1 2 Inactivated Vaccine
04:46 -
11. 1. 3 Subunit Vaccine
05:38 -
11. 1. 4 Recombinant Vector and DNA Vaccines
05:49 -
11 .1. 5 Epitope Based Vaccines
05:47 -
11. 2 Band TCell Epitopes
06:12 -
11 .3 Bioinformatics in Vaccine Design
05:52 -
11 .4 Prediction Tools for Class I and II MHC Binding
07:02 -
11. 5 CTL Epitope Prediction
08:15 -
11. 5 .1 NetCTL
05:01 -
11 .7 Methods for In Silico Designing of Epitope Based Vaccines
05:17 -
11. 7. 1 Selection of Proteins
06:04 -
11. 7. 2 Epitope Prediction and Analysis
06:57 -
11. 7. 3 Molecular Docking and Molecular Dynamics Simulation
06:22 -
11 .7. 4 Construction of Vaccine
07:00 -
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
08:12 -
11. 9 Limitations and Challenges
08:03 -
11. 10 In Silico Designing of Vaccines Methods, Tools, and Their Limitations
04:39
Chapter 12
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Abstract ….Machine Learning Approaches to Rational Drug Design
04:55 -
12. 1 Drug Industry
00:00 -
12. 2 Drug Discovery Pipeline
07:35 -
12. 3 Complexity of the Problem and Role of ML Techniques
06:36 -
12. 4 Genetic Algorithms
06:52 -
12 .4. 2 Genetic Algorithm Operators
06:19 -
12. 5 Artificial Neural Networks
07:25 -
12. 6 Deep LearningDL
07:27 -
12. 7 Support Vector Machines
04:11 -
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
04:35 -
12. 9. 2 Pharmacophore Modeling
06:45 -
12. 9 .3 Quantitative Structure–Activity Relationship QSAR
05:37 -
10 Conclusions
04:38
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