Chapter
Chapter 1: An Introduction to Computational Phytochemistry
1.2 Computational Phytochemistry
1.3. Techniques, Theories, and Applications of Computational Phytochemistry
1.3.1 Kohonen-Based Self-Organizing Map
1.3.2 Density Functional Theory
1.3.3 Docking Experiments and Virtual Screening (In Silico Screening)
1.3.4 Structure Prediction and Structure Determination
1.3.5 Chemometrics and Principal Component Analysis
1.3.6 Data Mining and Databases
1.3.7 Response Surface Methodology in Optimization of Extraction of Phytochemicals
1.3.8 Computation in Isolation of Phytochemicals
Chapter 2: Prediction of Medicinal Properties Using Mathematical Models and Computation, and Selection of Plant Materials
2.3. Computational Models in Drug Discovery
2.3.1 Structure-Based CADD
2.3.3 Network Pharmacology
2.4. Selection of Medicinal Plants
2.4.1 Ethnobotany-Directed Drug Discovery
2.4.2 Chemotaxonomic and Ecological Approach
2.4.4 Integrated Approach
2.5 Role of Medicinal Plants Databases
2.7 Role of Data Mining in Medicinal Plant Selection
2.8 Safety Considerations
Chapter 3: Optimization of Extraction Using Mathematical Models and Computation
3.2. Fundamentals of Design of Experiments
Full Factorial Design (2k)
Fractional Factorial Design (2k−p)
3.2.2.2 Optimization Phase
Terminologies We Need to Know
Sequential Nature of Response Surface Methodology
3.3 DoE-Based Optimization of MAE Process
3.4 DoE-Based Optimization of Supercritical Fluid Extraction Process
3.5 DoE-Based Optimization of Accelerated Solvent Extraction Process
Chapter 4: Application of Computational Methods in Isolation of Plant Secondary Metabolites
4.2. Computational Methods in Natural Products Isolations
4.2.1 Automated Flash Chromatography
4.2.2 High-Performance/Pressure Liquid Chromatography
4.2.3 Ultra-Pressure/Performance Liquid Chromatography
4.2.4 Counter Current Chromatography
4.2.5 Capillary Electrophoresis
4.2.6 Hyphenated Techniques
Chapter 5: Application of Computation in Building Dereplicated Phytochemical Libraries
5.2.1 Combinatorial Library
5.2.2 Phytochemical Library
5.4 Application of Computation in Building Dereplicated Phytochemical Libraries
Chapter 6: High-Throughput Screening of Phytochemicals: Application of Computational Methods
6.3. High-Throughput Screening
6.3.1 Reaction Monitoring and Observation
6.3.2 Advances in Monitoring In Vivo
6.3.3 Location of Facilities
6.3.4 Is There a Difference Between So-Called Leads and Drugs?
6.3.5 Visualization of Data
6.3.6 Dose–Response Analysis
6.3.7 Examples of HTS Success
6.4. HTS Platforms for Natural Products/Phytochemicals
6.4.1 What is a Natural Product?
6.4.2 Natural Products for Increasing Diversity
6.4.3 Natural Products Sample Preparation
6.4.4 Examples of HTS Platforms for Natural Products/Phytochemicals
6.5 High-Content Screening
Chapter 7: Prediction of Structure Based on Spectral Data Using Computational Techniques
7.1.1 History of Spectroscopy
7.1.2 Misassignments of Structures: A Rarity or More Common Than Expected?
7.2 Structure Elucidation Strategies
7.3 What is Density Functional Theory?
7.4. Era of Assignment Versus Prediction
7.4.1 Nuclear Magnetic Resonance
7.4.2 Computational Mass Spectrometry
7.4.4 Structure by Calculations
7.4.6 Infrared (IR) Spectroscopy
7.4.7 Database Search Algorithm
7.5 Can Raman Be Used for Automated Assays and HTS?
7.6 X-Ray Sponge Technique
Chapter 8: Application of Mathematical Models and Computation in Plant Metabolomics
8.2 Create Clarity From Chaos—Mindset
8.4. Experimental Considerations
8.4.1 Data Collection Considerations
8.4.4 Analysis Modalities
8.4.5 Throughput in Plant Metabolomics
8.5.2 Unsupervised Approach
8.5.3 Supervised Approach
8.5.3.1 Linear Regression
8.5.3.2 Discriminant Analysis
8.5.3.3 Tree-Based Methods
8.5.3.4 Performance Considerations: Validation
8.6 Metabolomics in Agriculture
Chapter 9: Application of Computation in the Biosynthesis of Phytochemicals
9.3. Computational Tools and Databases for Identification and Analysis of BGCs and Secondary Metabolites
9.4. Computational Tools for Metabolomics Study
9.5. Tools for Prediction of Biochemical Pathways
9.5.1 From Metabolite to Metabolite
9.5.2 Biochemical Network-Integrated Computational Explorer
9.5.5 Cho System Framework
9.6. Chemical Compound Databases
9.6.1 Dictionary of Natural Products
9.7. Overview and Conclusions
Chapter 10: Computational Aids for Assessing Bioactivities
10.1 Introduction: Computational Aids in Science and Their Role in Bioactivity Studies of Natural Products
10.2 Strategies for Separation and Identification of Bioactive Natural Compounds for Drug Discovery
10.3. Bioactivity Assessment in Phytochemistry
10.3.1 Protein-Based In Vitro Models
10.3.2 In Vitro Cell Culture Models
10.3.3 In Situ and ex vivo Models
10.4 Computational Tools for Data Analysis From Metabolomics and Bioactivity Assessment Data in Natural Product Research ...
10.5 Data- and Text-Mining Strategies
10.6 Virtual or In Silico Screening of Natural Products
10.7 Application Example of an In Silico Assessment of Bioactivities on the Example of the Cannabinoid Receptor 2
10.8 Overview of Software and Web-Tools for Bioactive Phytochemicals Research
Chapter 11: Virtual Screening of Phytochemicals
11.1.1 Artificial Neural Networks (ANNs)
11.1.2 Application of ANNs in Pharmaceutical Science
11.1.3 ANNs in Predicting Bioactivity
11.1.4 Gossypol and its Derivatives
11.2. Materials and Methods
11.2.1 Input and Output Vector Definition for Data
11.2.2 Software and Hardware Environment
11.2.3 Modelling Procedure
11.2.4 Training and Test Data Set
11.2.5 Experimental Data Set
11.2.6 Docking Experiment
11.3 Results and Discussion
Computational Phytochemistry
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