Course Prerequisite(s)
- Please note that this course has the following prerequisites which must be completed before it can be accessed
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1 – Introduction to Chemoinformatics and Computational Drug Discovery
About Course
Description
This book provides a comprehensive guide to the practical applications of chemoinformatics in drug discovery and molecular modeling. It covers methodologies such as molecular descriptors, virtual screening, and QSAR (Quantitative Structure-Activity Relationship) modeling, offering hands-on protocols and case studies for researchers. The book is a valuable resource for understanding how computational tools are transforming chemical research and drug development.
Implementation Plan for the Book Club Over Two Months
1. Book Selection
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Book: Practical Chemoinformatics.
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Level: Intermediate to Advanced.
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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 Chemoinformatics) + Chapter 2 (Molecular Descriptors).
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Week 2: Chapter 3 (Chemical Databases) + Chapter 4 (Data Mining in Chemoinformatics).
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Week 3: Chapter 5 (Virtual Screening Techniques) + Chapter 6 (Molecular Docking).
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Week 4: Chapter 7 (QSAR Modeling) + Chapter 8 (Pharmacophore Modeling).
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Week 5: Chapter 9 (Case Studies in Drug Discovery) + Chapter 10 (Challenges in Chemoinformatics).
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Week 6: Chapter 11 (Future Directions) + 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 chemoinformatics 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 chemoinformatics and its role in drug discovery.
- Learn techniques for molecular descriptor calculation and virtual screening.
- Gain practical skills in using chemoinformatics tools and software.
- Explore QSAR modeling and pharmacophore modeling methodologies.
- Apply computational methods to solve chemical research challenges.
Course Content
Before You Start: Book Club Orientation
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Practical Chemoinformatics | Book Club
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Practical Chemoinformatics | Book Club
Chapter 1
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1 Abstract
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1 .1 Chemoinformatics
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1. 1 .1 Open-Source Tools 2
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1 .1 .2 Introduction to Programming Languages
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1 .2 Chemical Structure Representation
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1 .3 Code for Including the Editor Applet in JChemPaint
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1. 4 Definition of Templates
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1 .5 Free Tools
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1. 7 Commercial Tools
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1. 8 A Practice Tutorial
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1 .9 Introduction to Chemical Structure Formats
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1. 9 .2 Graph based Representation 2D and 3D formats
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1 .9. 4 FILE FORMATS
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1. 10 2D and 3D Representation
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1. 11 Abstract Representation of Molecules
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1 .12 File Format Exchange
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1. 14 Molecular Similarity
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1. 15 Search for Relationship
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1 .16 Similarity Measures
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1. 17 Molecular Diversity
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1 .18 Advanced Structure handling Tools
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1 .19 ChemXtreme
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1 .20 Virtual Library Enumeration
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1 .21 Clustering
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1. 22 Databases
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1. 22. 7 Chemical Databases
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1. 22 .8 Do It Yourself DIY
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Chapter 2
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Chemoinformatics Approach for the Design and Screening of Focused Virtual Librarie
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2 .1 Introduction to Structure–Property Correlations
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2. 1 .1 Descriptors
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2. 1. 1 .2 Free Programs
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2. 1. 1. 3 Tools Requiring An Academic License
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2.1.1.4 Commercial Software to Calculate Molecular
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2.1.1.5 In House Developed Open Source Tool
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2.1.2 Online Property Prediction Tools
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2. 1. 3. 1 Virtual Library Generation Enumeration
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2 .1. 3 .2 Open Source Tools for Virtual Library Synthesis SmiLib
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2.1.4 Virtual Screening
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2. 1. 4. 1 Free Virtual Library Screening Platforms
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2.1.5 Thumb Rules for Computing Molecular Properties
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2. 1. 6 Do it Yourself
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END OF Chapter 2 Chemoinformatics Approach for the Design
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Chapter 3
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Chapter 3 Machine Learning Methods in Chemoinformatic
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3. 1 Introduction
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3 .2 Machine Learning Models for Predictive Studies
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3. 3 Machine Learning Methods
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3. 4 Open Source Tools for Building Models for Drug Design
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3 .4. 2 Waikato Environment for Knowledge Analysis WeKa
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3. 4 .2 .1 A Tutorial for Building Classification Models Using LibSVM and Weka
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3. 4. 2. 2 Obtaining Accuracy Using Random Forest
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3. 4 .3 R Program
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3. 5 Free Tools for Machine Learning
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3. 5 .2 Rapid Miner
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3. 5. 2. 1 Practice Tutorial for Building Machine Learning Models in Rapid Miner
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3. 6 Commercial Tools for Building ML Models
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3 .6. 1. 1 A Tutorial for QSAR Model Building of DHFR Inhibitors
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3. 6. 2 IBM SPSS
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3. 6. 3 Matrix Laboratory MATLAB
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3 .7 Genetic Programming Based ML Models
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3 .7 .1 A Practical Demonstration of GP Based Software
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3. 8 Thumb Rules for Machine Learning Based Modelling
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Machine Learning Methods in Chemoinformatic
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Chapter 4
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1 Abstract
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1 Introduction
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4.2 A Practice Tutorial Docking
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4. 3 Docking Using Open Source Software
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4. 3 .1 Autodock Steps
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4 .3. 2 Docking Using AutoDock Vina
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4. 4 Other Docking Algorithms
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4. 4. 1 Induced Fit Docking
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4 .4 .2 Flexible Protein Docking
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4 .4. 3 Blind Docking
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4 .4. 4 Cross Docking
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4. 4. 5 Docking and Site Directed Mutagenesis
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4 .5 Protein–Protein Docking
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4.6 Pharmacophore
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4 .6. 1 Pharmacophore Modelling in SCHRÖDINGER
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4. 6. 2 Finding Pharmacophore Features Using MOE
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4 .8 Rules of Thumb for Structure Based Drug Design
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4. 9 Do it Yourself Exercises
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Chapter 5
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Abstract
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5 .1 Introduction
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5 .2 A Practice Tutorial for Predicting Active Site Using SiteMap
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5 .3 A Practice Tutorial for Active Site Prediction Using MOE
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5 .4 Free Online Tools for Active Site Prediction
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5. 5 Homology Modelling
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5. 6 A Practice Tutorial for Homology Modelling
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5. 7 Model Validation Using Online Servers
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5. 8 Receptor Based Pharmacophore
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5. 9 Studies on Active Site Structural Features
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5. 9. 1 Application of Active Site Features in Chemoinformatics
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5. 9. 1. 2 Code for Obtaining the Ligand Coordinates X, Y, Z values from
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5. 10 Thumb Rules for Active Site Identification and Homology Modelling
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END ( Active Site Directed Pose Prediction Programs for Efficient Filtering of Molecules)
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Chapter 6
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Abstract
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6 .1 Introduction
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6 .3 Computational Methods in Reaction Modelling
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6. 4 TS Modelling of Organic Transformations
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6. 4. 2 A Practice Tutorial for Transition State and Intrinsic Reaction Coordinate Modelling
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6. 4. 3 A Practice Tutorial Using Maestro–Jaguar
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6. 4. 4 A Practice Tutorial Using Spartan
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6 .5 Reaction Searching Approaches and Tools
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6. 5 .1 Chemical Ontologies Approach for Reaction Searching
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6 .5. 2 Reaction Searching Using Fingerprints Based Approach
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6 .5 .3 Tools for Reaction Searching
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6. 6 Reaction Databases
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6. 6 .1 Tools for Reaction Library Enumeration
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6. 6. 2 A Practice Tutorial
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6. 7 Artificial Intelligence in Chemical Synthesis
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6. 8 Modelling Enzymatic Reactions
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6. 9 Thumb Rules for Performing Reaction
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10 Do it You
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Chapter 7
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Abstract
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7 .1 Introduction
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7 .2 Fragment Based Drug Discovery
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7 .3 Spectra Prediction Methods
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7 .4 Spectra Prediction Tools
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7. 5 Open Source Tools 7 5
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7. 6 Proprietary Tools
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7. 6. 4 Gaussian
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7 .6 .5 ADF
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7. 6 .7 Spartan
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7. 6. 8 Spectral Databases 7 6 8
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7 .7 Spectra Viewer Programs
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7 .11 Do it Yourself
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Chapter 8
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Abstract
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8. 1 What is Text Mining
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8 .1. 1 Text Mining vis a vis Data Mining
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8. 1. 2 A Snippet of Java Code Using the Above URL
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8. 2 What are the Components of Text Mining
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8 .3 Text mining Methods
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8 .4 Why Text Mining
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8. 5 General Text mining Tools
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8 .5. 1 A Practice Tutorial with an Open source Tool
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8. 5 .2 R Program for Text Mining
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8. 6 Free Tools for Text Mining
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8 .7 Biomedical Text Mining
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8 .8 Chemically Intelligent Text mining Tools
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8. 9 In house Tools for Text mining Applications for Chemoinformatics
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8. 9. 1 Java Code Snippet for Data
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8 .10 Thumb Rules While Performing and Using Text mining Result
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8. 11 Do it Yourself
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Chapter 9
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Abstract
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9. 1 What is a Workflow
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9 .2 Need for Workflows
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9. 3 General Workflows in Bioinformatics
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9 .4 General Workflows in Chemistry Domain
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9. 4. 4 KNIME
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9 .4. 5 Workflow Examples
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9. 5 Schrodinger KNIME Extensions
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9 .5. 1 A Practice Tutorial
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9. 6 Other KNIME Extensions
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9 .6 .2 ChemAxon
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9 .7 Protein–Ligand Analysis Based Workflows for Drug Discovery
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9 .7. 1 A Practice Tutorial for Protein–Ligand Fingerprint Generation
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9 .8 Prolix
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9 .9 J ProLINE An In house developed Chem Bioinformatics Workflow Application
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9 .10 Targetlikeness Score
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9 .11 Databases and Tools
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9 .12 Thumb Rules for Generating and Applying Workflows
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9 .13 Do it
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Chapter 10
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Abstract
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10. 1 What is a Portal
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10. 2 Need for Development of Scientific Portals
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10 .3 Components of a Portal
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10 .4 Examples of Portal Systems
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10. 5 A Practice Tutorial for Portal Creation
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10. 5. 1 Custom Database connection and Display Table with
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10. 6 A Practice Tutorial for Development of Portlets for Chemoinformatics
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10. 6. 2 JME Portlet
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10. 6 .3 Jchempaint Portlet
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10. 7 Mobile Computing
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10. 7. 1 Android Applications for Chemoinformatics
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10 .8 Need of High Performance Computing in Chemoinformatics
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10 9 Thumb Rules for Developing and Using Scientific Portals and Mobile Devices for Computing
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10 10 Do it
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REVIW
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