Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0
- 1st Edition - February 1, 2027
- Latest edition
- Editors: Garikapati Narahari Sastry, Hridoy Jyoti Mahanta, Selvaraman Nagamani, Dinadayalane Tandabany
- Language: English
Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0 explores the cutting-edge synergy among Computational Chemistry, Artificial Intell… Read more
Description
Description
Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0 explores the cutting-edge synergy among Computational Chemistry, Artificial Intelligence (AI), and the emerging paradigm of Industry 5.0. The book offers a comprehensive, introductory overview of how AI-driven techniques are revolutionizing the field of computational chemistry and transforming industries. Readers will explore the convergence of AI algorithms, big data analytics, and advanced computational methods such as Natural language Processing, Image Processing, and Machine Learning in the context of chemical research and industrial processes. The book also discusses how AI is accelerating Computational Chemistry, Materials Science, and Chemical Engineering by automating complex calculations, predicting molecular properties, and optimizing chemical processes. Furthermore, it provides a deep dive into the concept of Industry 5.0, which envisions a new era of manufacturing characterized by human-robot collaboration, intelligent factories, and decentralized production systems. The book illustrates how AI and Computational Chemistry play pivotal roles in realizing the vision of Industry 5.0 by optimizing manufacturing processes, quality control, and sustainability efforts.
Key features
Key features
- Encompasses both the technical aspects of computational chemistry and the broader implications for industries and society at large
- Offers clear explanations of complex AI algorithms used in computational chemistry, making it accessible to both experts and newcomers to the field
- Helps readers gain insights into real-world applications of Industry 5.0 principles, where AI and automation are transforming manufacturing
- Explores how smart factories are enhancing efficiency, quality control, and sustainability, and how these innovations are reshaping the future of production in diverse sectors
- Delves into case studies that showcase how AI is revolutionizing materials design, leading to the development of novel, high-performance materials for industries ranging from electronics to aerospace
Readership
Readership
Graduate and postgraduate students and their tutors focused on computational chemistry and cheminformatics
Table of contents
Table of contents
Part 1. Artificial Intelligence
1. A Comprehensive Introduction to AI
2. Chemical Space and AI
3. Impact of AI in Computational Chemistry
4. Machine Learning Applications in Computational Chemistry
4.1 ML and QSAR
4.2 Property Prediction
4.3 Generative Models for Molecular Design
4.4 Chemical Reactions
5. AI-Driven Approaches in Quantum Chemistry
5.1 Quantum Property Prediction
5.2 Quantum Circuit Optimization
5.3 Quantum Machine Learning for Molecular Systems
6. Future and Challenges of AI in Chemistry
Part 2. Machine Learning
7. Fundamental Concepts of Machine Learning
8. Understanding the Foundations
8.1 Human Learning and Types of Human Learning
8.2 Human versus Machine Learning
8.3 Supervised and Unsupervised Learning
8.4 Neural Networks and Deep Learning
8.5 Generative Learning
9. Essential Steps in Applying Machine Learning
9.1 Preparation and Handling of Data
9.2 Feature Engineering and Feature Selection
9.3 Model Building and Validation
9.4 Evaluation
9.5 Applicability Domain and Deployment
10. Machine Learning in Various Fields of Natural Sciences
10.1 Material Sciences
10.2 Chemical Sciences
10.3 Life Sciences
10.4 Environmental Sciences
10.5 Agricultural Sciences
11. From Machine Learning to Deep Learning
12. Rise of Generative Models and Industry 5.0
Part 3. Scientific Computing Using Python
13. Python Basics
13.1 Creating Python Environment
13.2 Installation of Packages and Libraries
13.3 Python Workbenches
14. Handling Numeric Data with NumPy
14.1 Basic Computing Operations
14.2 Arrays and Indexing
14.3 Vectorization
14.4 Functions and Methods
14.5 Dealing with Missing Data
14.6 Generating Random Numbers
15. Utilities of Pandas
15.1 Reading and Handling Data with Pandas
15.2 Selecting and Indexing
15.3 Advanced Indexing
15.4 Handling Text Data
15.5 Statistical Functions with Pandas
16. Visualization with Matplotlib and Seaborn
16.1 Plotting Basics
16.2 Various Types of Plots in Matplotlib
16.3 Overlaying and Multifigure Plots
16.4 3-Dimensional Plotting
16.5 Seaborn Plot Types
16.6 Categorical Plots
16.7 Distribution and Pair Plots
16.8 Correlation and Heatmaps
17. RdKit for Chemoinformatics
17.1 Molecule Representations
17.2 Visualizing Structures
17.3 Basic Operations with RdKit
17.4 Finding Descriptors
17.5 Atoms and Bonds
17.6 Similarity and Searching Patterns
18. Chemypy Package
18.1 Basics and Installation
18.2 Handling Reactions
18.3 Chemical Kinetics
18.4 Finding Properties
18.5 Other Utilities
Part 4. Machine Learning with Python
19. Scikit-learn Library in Python
20. Data Representation and Generation
20.1 Generating Synthetic Data
20.2 Exploring Data Sets
20.3 Data Preprocessing and Preparation
20.4 Feature Engineering
21. Supervised Machine Learning
21.1 Training for Linear Regression
21.2 Multi-Linear Regression
21.3 Classification with Logistic Regression
21.4 Random Forest-Based Classification
22. Unsupervised Machine Learning
22.1 Dimensionality Reduction
22.2 Clustering with Partitioned Algorithms
22.3 Hierarchical Clustering
23. Evaluation Metrics
23.1 Confusion Matrix
23.2 Accuracy and Error
23.3 Precision and Recall
23.4 ROC-AUC
23.5 Cluster Analysis Metrics
24. Case Studies
Part 5. Evolution of Computational Chemistry
25. Overview of Computational Chemistry
25.1 Computational Tools and Techniques
25.2 Significance and Contributions
26. Era of High-Performance Computing
26.1 Role of Supercomputing in Computational Chemistry
26.2 Parallelization and Acceleration Techniques
26.3 Cloud Computing and Distributed Computing
27. Software and Tools
27.1 Overview of Computational Chemistry Software
27.2 Open-Source vs. Commercial Software
27.3 Popular Software Packages and Their Capabilities
27.4 Intersection of Machine Learning and Computational Chemistry
27.5 Predictive Modelling and Property Estimation
28. Recent Advances and Future Directions
28.1 Quantum Computing and Its Impact
28.2 Multiscale Modelling and Simulation
28.3 Emerging Fields and Interdisciplinary Applications
Part 6. Structure-Property Relationships
29. Fundamentals of Structure-Property Relationships
29.1 Defining Structure and Property
29.2 Understanding Structure-Property Relationships
29.3 Interlinking Molecular/Structural Features and Properties
30. Chemical Structure and Property Correlations
30.1 Molecular Structure and Properties
30.2 Electronic Structure and Optical Properties
30.3 Topological and Geometrical Descriptors
31. Quantitative Structure-Property Relationships (QSPR)
31.1 Developing QSPR Models
31.2 Regression Analysis and Parameterization
31.3 Applicability and Limitations
32. Quantitative Structure-Activity Relationships (QSAR)
32.1 QSAR in Drug Design
32.2 Molecular Descriptors in QSAR
32.3 Predictive Modelling and Toxicology
33. Materials Science and Structure-Property Relationships
33.1 Atomic and Crystal Structures
33.2 Mechanical Properties of Materials
33.3 Thermodynamic and Electronic Properties
34. Biological Systems and Structure-Property Relationships
34.1 Proteins and Enzymes
34.2 DNA and RNA
34.3 Structure-Function Relationships in Biology
Part 7. Reaction Modelling
35. Overview of Reaction Modelling
36. Chemical Kinetics
36.1 Basics of Chemical Reactions
36.2 Reaction Rate and Rate Laws
36.3 Factors Affecting Reaction Rates
37. Reaction Mechanisms
37.1 Elementary Reactions vs. Overall Reactions
37.2 Reaction Intermediates
37.3 Reaction Mechanism Determination
37.4 Analysis of Reaction Potential Energy Surface
38. Reaction Rate Constants
38.1 Arrhenius Equation
38.2 Temperature Dependence
38.3 Catalysis and Reaction Rate Constants
39. Reaction Modelling Approaches
39.1 Homogeneous vs. Heterogeneous Reactions
39.2 Batch, Plug Flow, and Continuous Stirred Tank Reactors
39.3 Ideal vs. Non-Ideal Reactors
40. Numerical Methods for Reaction Modelling
40.1 Finite Difference Methods
40.2 Finite Element Methods
40.3 Computational Fluid Dynamics (CFD)
Part 8. Computer-Aided Drug Design
41. Introduction to Computer-Aided Materials (Drug) Design
42. Drug Discovery Process
42.1 Target Identification and Validation
42.2 High-Throughput Screening (HTS)
42.3 Hit-to-Lead Optimization
42.4 Lead Optimization and Preclinical Testing
43. Molecular Modeling in Drug Design
43.1 Protein Structure Prediction
43.2 Ligand Docking and Binding Affinity Prediction
43.3 Pharmacophore Modeling
43.4 Quantitative Structure-Activity Relationship (QSAR) Studies
44. Virtual Screening and Compound Selection
44.1 Structure-Based Virtual Screening
44.2 Ligand-Based Virtual Screening
44.3 Fragment-Based Drug Design
45. De Novo Drug Discovery
45.1 De Novo Molecular Design
45.2 Computer-Generated Molecule Libraries
45.3 Optimization Algorithms in Rational Drug Design
46. Chemoinformatics and Bioinformatics
46.1 Molecular Databases and Data Mining
46.2 Sequence Analysis in Drug Discovery
46.3 Chemoinformatics for Compound Analysis
47. ADME/Toxicity Prediction
47.1 Absorption, Distribution, Metabolism, and Excretion (ADME)
47.2 Predicting Drug Toxicity
47.3 Risk Assessment in Drug Design
Part 9. Materials Modelling
48. Introduction
48.1 Role of Materials Modelling in Science and Engineering
48.2 Overview of Computational Methods
49. Materials
49.1 Predicting Mechanical Properties
49.1.1 Strength and Elasticity
49.1.2 Ductility and Toughness
50. Material Design for Specific Applications
50.1 Aerospace Materials
50.2 Automotive Materials
50.3 Building and Construction Materials
51. Electronic and Photonic Materials
51.1 Semiconductor Device Simulation
51.1.1 Transistor Design
51.1.2 Optoelectronic Device Modelling
52. Superconductors and Magnetic Materials
52.1 High-Temperature Superconductors
52.2 Magnetic Data Storage Materials
53. Energy Materials
53.1 Fuel Cell and Battery Materials
53.1.1 Lithium-Ion Batteries
53.1.2 Fuel Cell Catalysts
53.2 Solar Cell Materials
53.2.1 Photovoltaic Device Optimization
53.2.2 Organic Solar Cells
54. Nanomaterials and Nanotechnology
54.1 Modeling at the Nanoscale
54.2 Nanoparticle Synthesis and Properties
54.3 Nanocomposite Materials
Part 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation
55. Introduction to Quantum Mechanics
55.1 Wave Functions, Probability Densities and Operators
55.2 Postulates of Quantum Mechanics
55.3 The Time-Independent Schrödinger Equation
56. Molecular Hamiltonians and Operators
56.1 Born-Oppenheimer Approximation
56.2 Hamiltonian Operators
56.3 Expectation Values and Observables
57. Basis Sets and Wave Function Expansions
57.1 Atomic Orbitals and Basis Functions
57.2 Gaussian Basis Sets
57.3 Slater-Type Orbitals (STOs)
57.4 Types of Basis Sets
57.5 Plane Waves and Fourier Transforms
58. Introduction to Ab Initio Calculations
58.1 Hartree-Fock Theory
58.2 Configuration Interaction (CI)
58.3 Many-Body Perturbation Theory (MBPT)
59. Coupled Cluster Theory
59.1 Cluster Operators and Excitations
59.2 Single and Double Excitations (CCSD)
59.3 Higher-Order Excitations (CCSD(T))
60. Density Functional Theory (DFT)
60.1 Hohenberg−Kohn Theorem
60.2 Kohn-Sham Equations
60.3 Local Density Approximation (LDA)
60.4 Generalized Gradient Approximation (GGA)
60.5 Dispersion Corrected DFT
60.6 Hybrid Functional Theory
61. Advanced Topics in DFT
61.1 Time-Dependent DFT (TDDFT)
61.2 Linear Response and Excitation Energies
61.3 Optical Properties and Spectroscopy
61.4 Beyond TDDFT: Nonlinear Response
62. DFT for Strongly Correlated Systems
62.1 Hubbard U and DFT+U
62.2 Dynamical Mean Field Theory (DMFT)
62.3 Quantum Monte Carlo and DFT+QMC
63. Molecular Dynamics (MD) Simulation
63.1 Introduction
63.2 MD Using Simple Models
63.3 MD with Continuous Potentials
63.4 MD at Constant Temperatures and Pressures
63.5 MD with Solvent Effects: Mean Force and Stochastic Dynamics
63.6 Conformational Changes Post MD Simulation
64. Quantum Mechanics/Molecular Mechanics (QM/MM)
64.1 Hybrid Methods Overview
64.2 Implementation and Applications
64.3 Challenges and Considerations
65. Advanced MD Techniques
65.1 Umbrella Sampling
65.2 Metadynamics
65.3 Replica Exchange Molecular Dynamics (REMD)
65.4 Ab Initio Molecular Dynamics (AIMD)
66. Machine Learning in MD
66.1 Force Field Parametrization
66.2 Enhanced Sampling with ML
66.3 Other Important Methods
67. Simulation of Biomolecular Complexes
67.1 Protein-Ligand Complexes
67.2 Protein-Nucleic Acid Interactions
67.3 Protein-Protein Interactions: Large Protein Assemblies and Their Interactions
67.4 Membrane Proteins and Lipid Bilayers: Proteins Embedded in Lipid Membranes
Part 11. The Chemical Space
68. The Concept of Chemical Space
69. Importance in Chemistry and Beyond
70. The Chemical Spaces
70.1 Chemical Space for Pharmacy
70.2 Pharmacophore Space
70.3 Polypharmacology
70.4 Chemical Space for Natural Products
70.5 Chemical Space for Biology and Medicine
71. Docking for Virtual Screening of Chemical Space
71.1 Different Open-Source Docking
71.2 ML and DL Docking Tools for Chemical Space
72. Chemoinformatic Resources for Chemical Space
72.1 Various Chemical Space Databases
72.2 Open-Source Platforms for Chemoinformatics
72.3 Online Tools Developed for Mining Chemical and Target Spaces
72.4 Useful Servers for Mining Chemical and Target Spaces of Target Families or Diseases
73. Dimensions of Chemical Space
73.1 Structural-Based Dimensions
73.2 Descriptor-Based Dimensions
74. Advanced Approaches to Explore the Chemical Space
75. AI-ML Techniques and Tools for Chemical Space
Part 12. Generative Models for Novel Catalyst Design
76. Introduction
77. Foundations of Catalyst Design
77.1 Background and Significance of Catalyst Design
77.2 The Evolution of Catalyst Development Approaches
77.3 Role of Generative Models in Accelerating Catalyst Innovation
77.4 Traditional Catalyst Discovery Methods and Limitations
77.5 Need for Accelerated and Targeted Catalyst Design
78. Generative Models in Chemistry
78.1 Overview of Generative Models
78.2 Machine Learning and Deep Learning in Chemistry
78.3 Applications of Generative Models in Catalyst Design
79. Types of Generative Models
79.1 Variational Autoencoders (VAEs)
79.2 Generative Adversarial Networks (GANs)
79.3 Reinforcement Learning in Catalyst Design
79.4 Comparative Analysis of Generative Model Types
80. Catalyst Property Prediction
80.1 Predictive Modeling for Catalyst Activity and Selectivity
80.2 Quantitative Structure-Activity Relationship (QSAR) Models
80.3 Challenges and Opportunities in Property Prediction
81. Molecular Representation and Embedding
81.1 Encoding Chemical Structures for Generative Models
81.2 Graph Neural Networks (GNNs) in Catalyst Design
81.3 Embedding Techniques for Catalyst Descriptor Generation
82 Challenges and Considerations
82.1 Data Quality and Bias in Training Datasets
82.2 Transferability and Generalization of Generative Models
82.3 Ethical Considerations in AI-Driven Catalyst Design
83. Future Directions and Emerging Technologies
83.1 Advanced Generative Models on the Horizon
83.2 Integration with High-Throughput Experimentation
83.3 Collaborative Approaches in Catalyst Design
Part 13. Transforming Petroleum and Polymers Industry with AI
84. Petrochemicals as Sustainable Materials for the Modern World
85. Polymers
85.1 Polymerization Process
85.2 Polymer Synthesis from Petrochemicals
85.3 Polymer Characterization
85.4 Role of Catalyst in Polymerization
86. Advanced Polymer Materials
86.1 High-Performance Polymers for Specialized Applications
86.2 Bio-Based and Sustainable Polymers
86.3 Polymer Composites for Enhanced Properties
87. Applying Machine Learning for Polymer Research
87.1 Polymer Representations
87.2 Generating New Polymer Chemistries
87.3 Prediction of Properties for Sequence Defined Polymers
87.4 Polymer Composites for Enhanced Properties
87.5 Autonomous Experimentation
88. Membrane Design for Petroleum Research
88.1 Membrane Materials
88.2 Membrane Types and Usage
88.3 Revolutionizing Membrane Design Using Machine Learning
88.4 Topology Optimization
88.5 Predictive Models for Water Permeability and Salt Rejection
89. Interpretable Discovery of Innovative Polymers and Membranes with AI
Part 14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery
90. Introduction to Natural Products Drug Discovery
90.1 Overview of Traditional Drug Discovery Methods
90.2 Importance of Natural Products in Drug Development
90.3 Challenges in Natural Products Drug Discovery
91. Data Integration and Analysis
91.1 Integration of Biological and Chemical Data
91.2 Computational Approaches for Natural Products Screening
91.3 Data Mining Techniques in Drug Discovery
92. Predictive Modeling in Natural Products Research
92.1 Predictive Modeling for Bioactivity
92.2 Structure-Activity Relationship (SAR) Analysis
92.3 QSAR (Quantitative Structure-Activity Relationship) Models
93. Target Identification and Validation
93.1 Identification of Drug Targets Using AI
93.2 Validation of Targets in Natural Products Drug Discovery
93.3 Challenges and Solutions in Target Identification
94. Database Development for Natural Products
94.1 Necessity for the Natural Products Databases
94.2 NEIMPDB and OSADHI
95. Prediction of Targets and Biological Activity of Natural Products
96. Visualizing and Navigating the Natural Products Space in Chemical Space
97. The Natural Product Database Landscape
Part 15. Applying Machine Learning in Drug Repurposing
98. Drug Repurposing
98.1 Overview of Traditional Drug Development
98.2 Rationale for Drug Repurposing
99. Role of Machine Learning and Artificial Intelligence in Drug Repurposing
99.1 Evolution of AI in Drug Discovery
99.2 Machine Learning Approaches in Repurposing
99.3 Applications of AI in Identifying Repurposable Drugs
99.4 Data Integration and Analysis for Drug Repurposing
100. Integration of Biomedical Data Sources
100.1 Computational Approaches for Data Analysis
100.2 Utilizing Real-World Evidence (RWE) in Repurposing
101. Network Pharmacology and Drug Repurposing
101.1 Network-Based Approaches to Drug Repurposing
101.2 Analysis of Biological Pathways and Networks
101.3 Predictive Modeling in Network Pharmacology
102. Predictive Analytics for Drug Repurposing
102.1 Predictive Modeling Techniques
102.2 Machine Learning Algorithms for Prediction
102.3 Quantitative Structure-Activity Relationship (QSAR) in Repurposing
103. High-Throughput Screening and Virtual Screening in Drug Repurposing
103.1 Automation in High-Throughput Screening
103.2 Virtual Screening Using AI Algorithms
103.3 Integration of Experimental and Computational Screening
104. Identification of Novel Targets for Drug Repurposing
104.1 Target Identification Using AI
104.2 Validation of Targets for Repurposing
104.3 Challenges and Strategies in Target Identification
105 Combination Therapy and Synergistic Drug Repurposing
105.1 AI-Guided Combination Therapy Strategies
105.2 Identifying Synergistic Drug Combinations
105.3 Challenges and Opportunities in Combination Repurposing
106. Ethical and Regulatory Considerations in Drug Repurposing
106.1 Ethical Issues in AI-Driven Drug Repurposing
106.2 Regulatory Compliance and Safety Assessment
106.3 Balancing Innovation with Ethical and Legal Frameworks
107. Implications for the Future of Drug Repurposing with AI
Part 16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry
108. Introduction
109. Fourth Industrial Revolution and the Rise of Industry 5.0
110. Role of Artificial Intelligence (AI) in Industry 5.0
111. Towards an AI-Assisted, Automated Chemistry Lab
111.1 Robotics and AI
111.2 Concept of the "Self-Driving" Lab
111.3 Remaining Hurdles to Realize the Vision of Industry 5.0
111.4 Will AI Ever Replace Human Chemical Intuition?
112. AI in Industry 5.0: Driving Smart Manufacturing
112.1 The Role of AI in Predictive Maintenance and Quality Control
112.2 AI-Powered Robotics and Automation
112.3 AI-Driven Supply Chain Optimization
112.4 AI-Driven Materials Databases and Repositories
113. Challenges and Opportunities of Industry 5.0
114. Future Trends in Reaction Modelling
114.1 Advances in Computational Approaches
114.2 Integration of Machine Learning and AI
114.3 Emerging Applications and Challenges
114.4 Importance of Reaction Modelling in Science and Industry
115. Machine Learning for Materials Simulation
115.1 High Throughput Virtual Screening
115.2 Analyzing Experimental Data
115.3 Active Learning for Materials
116. Various Tools for Computational Chemistry Using AI
117. Evolution of Chemical and Biological Space Using AI
118. Open-Source Tools of Computational Chemistry Using AI, ML, and DL
119. Latest Interventions of AI in Computational Chemistry
120. Future Prospects
1. A Comprehensive Introduction to AI
2. Chemical Space and AI
3. Impact of AI in Computational Chemistry
4. Machine Learning Applications in Computational Chemistry
4.1 ML and QSAR
4.2 Property Prediction
4.3 Generative Models for Molecular Design
4.4 Chemical Reactions
5. AI-Driven Approaches in Quantum Chemistry
5.1 Quantum Property Prediction
5.2 Quantum Circuit Optimization
5.3 Quantum Machine Learning for Molecular Systems
6. Future and Challenges of AI in Chemistry
Part 2. Machine Learning
7. Fundamental Concepts of Machine Learning
8. Understanding the Foundations
8.1 Human Learning and Types of Human Learning
8.2 Human versus Machine Learning
8.3 Supervised and Unsupervised Learning
8.4 Neural Networks and Deep Learning
8.5 Generative Learning
9. Essential Steps in Applying Machine Learning
9.1 Preparation and Handling of Data
9.2 Feature Engineering and Feature Selection
9.3 Model Building and Validation
9.4 Evaluation
9.5 Applicability Domain and Deployment
10. Machine Learning in Various Fields of Natural Sciences
10.1 Material Sciences
10.2 Chemical Sciences
10.3 Life Sciences
10.4 Environmental Sciences
10.5 Agricultural Sciences
11. From Machine Learning to Deep Learning
12. Rise of Generative Models and Industry 5.0
Part 3. Scientific Computing Using Python
13. Python Basics
13.1 Creating Python Environment
13.2 Installation of Packages and Libraries
13.3 Python Workbenches
14. Handling Numeric Data with NumPy
14.1 Basic Computing Operations
14.2 Arrays and Indexing
14.3 Vectorization
14.4 Functions and Methods
14.5 Dealing with Missing Data
14.6 Generating Random Numbers
15. Utilities of Pandas
15.1 Reading and Handling Data with Pandas
15.2 Selecting and Indexing
15.3 Advanced Indexing
15.4 Handling Text Data
15.5 Statistical Functions with Pandas
16. Visualization with Matplotlib and Seaborn
16.1 Plotting Basics
16.2 Various Types of Plots in Matplotlib
16.3 Overlaying and Multifigure Plots
16.4 3-Dimensional Plotting
16.5 Seaborn Plot Types
16.6 Categorical Plots
16.7 Distribution and Pair Plots
16.8 Correlation and Heatmaps
17. RdKit for Chemoinformatics
17.1 Molecule Representations
17.2 Visualizing Structures
17.3 Basic Operations with RdKit
17.4 Finding Descriptors
17.5 Atoms and Bonds
17.6 Similarity and Searching Patterns
18. Chemypy Package
18.1 Basics and Installation
18.2 Handling Reactions
18.3 Chemical Kinetics
18.4 Finding Properties
18.5 Other Utilities
Part 4. Machine Learning with Python
19. Scikit-learn Library in Python
20. Data Representation and Generation
20.1 Generating Synthetic Data
20.2 Exploring Data Sets
20.3 Data Preprocessing and Preparation
20.4 Feature Engineering
21. Supervised Machine Learning
21.1 Training for Linear Regression
21.2 Multi-Linear Regression
21.3 Classification with Logistic Regression
21.4 Random Forest-Based Classification
22. Unsupervised Machine Learning
22.1 Dimensionality Reduction
22.2 Clustering with Partitioned Algorithms
22.3 Hierarchical Clustering
23. Evaluation Metrics
23.1 Confusion Matrix
23.2 Accuracy and Error
23.3 Precision and Recall
23.4 ROC-AUC
23.5 Cluster Analysis Metrics
24. Case Studies
Part 5. Evolution of Computational Chemistry
25. Overview of Computational Chemistry
25.1 Computational Tools and Techniques
25.2 Significance and Contributions
26. Era of High-Performance Computing
26.1 Role of Supercomputing in Computational Chemistry
26.2 Parallelization and Acceleration Techniques
26.3 Cloud Computing and Distributed Computing
27. Software and Tools
27.1 Overview of Computational Chemistry Software
27.2 Open-Source vs. Commercial Software
27.3 Popular Software Packages and Their Capabilities
27.4 Intersection of Machine Learning and Computational Chemistry
27.5 Predictive Modelling and Property Estimation
28. Recent Advances and Future Directions
28.1 Quantum Computing and Its Impact
28.2 Multiscale Modelling and Simulation
28.3 Emerging Fields and Interdisciplinary Applications
Part 6. Structure-Property Relationships
29. Fundamentals of Structure-Property Relationships
29.1 Defining Structure and Property
29.2 Understanding Structure-Property Relationships
29.3 Interlinking Molecular/Structural Features and Properties
30. Chemical Structure and Property Correlations
30.1 Molecular Structure and Properties
30.2 Electronic Structure and Optical Properties
30.3 Topological and Geometrical Descriptors
31. Quantitative Structure-Property Relationships (QSPR)
31.1 Developing QSPR Models
31.2 Regression Analysis and Parameterization
31.3 Applicability and Limitations
32. Quantitative Structure-Activity Relationships (QSAR)
32.1 QSAR in Drug Design
32.2 Molecular Descriptors in QSAR
32.3 Predictive Modelling and Toxicology
33. Materials Science and Structure-Property Relationships
33.1 Atomic and Crystal Structures
33.2 Mechanical Properties of Materials
33.3 Thermodynamic and Electronic Properties
34. Biological Systems and Structure-Property Relationships
34.1 Proteins and Enzymes
34.2 DNA and RNA
34.3 Structure-Function Relationships in Biology
Part 7. Reaction Modelling
35. Overview of Reaction Modelling
36. Chemical Kinetics
36.1 Basics of Chemical Reactions
36.2 Reaction Rate and Rate Laws
36.3 Factors Affecting Reaction Rates
37. Reaction Mechanisms
37.1 Elementary Reactions vs. Overall Reactions
37.2 Reaction Intermediates
37.3 Reaction Mechanism Determination
37.4 Analysis of Reaction Potential Energy Surface
38. Reaction Rate Constants
38.1 Arrhenius Equation
38.2 Temperature Dependence
38.3 Catalysis and Reaction Rate Constants
39. Reaction Modelling Approaches
39.1 Homogeneous vs. Heterogeneous Reactions
39.2 Batch, Plug Flow, and Continuous Stirred Tank Reactors
39.3 Ideal vs. Non-Ideal Reactors
40. Numerical Methods for Reaction Modelling
40.1 Finite Difference Methods
40.2 Finite Element Methods
40.3 Computational Fluid Dynamics (CFD)
Part 8. Computer-Aided Drug Design
41. Introduction to Computer-Aided Materials (Drug) Design
42. Drug Discovery Process
42.1 Target Identification and Validation
42.2 High-Throughput Screening (HTS)
42.3 Hit-to-Lead Optimization
42.4 Lead Optimization and Preclinical Testing
43. Molecular Modeling in Drug Design
43.1 Protein Structure Prediction
43.2 Ligand Docking and Binding Affinity Prediction
43.3 Pharmacophore Modeling
43.4 Quantitative Structure-Activity Relationship (QSAR) Studies
44. Virtual Screening and Compound Selection
44.1 Structure-Based Virtual Screening
44.2 Ligand-Based Virtual Screening
44.3 Fragment-Based Drug Design
45. De Novo Drug Discovery
45.1 De Novo Molecular Design
45.2 Computer-Generated Molecule Libraries
45.3 Optimization Algorithms in Rational Drug Design
46. Chemoinformatics and Bioinformatics
46.1 Molecular Databases and Data Mining
46.2 Sequence Analysis in Drug Discovery
46.3 Chemoinformatics for Compound Analysis
47. ADME/Toxicity Prediction
47.1 Absorption, Distribution, Metabolism, and Excretion (ADME)
47.2 Predicting Drug Toxicity
47.3 Risk Assessment in Drug Design
Part 9. Materials Modelling
48. Introduction
48.1 Role of Materials Modelling in Science and Engineering
48.2 Overview of Computational Methods
49. Materials
49.1 Predicting Mechanical Properties
49.1.1 Strength and Elasticity
49.1.2 Ductility and Toughness
50. Material Design for Specific Applications
50.1 Aerospace Materials
50.2 Automotive Materials
50.3 Building and Construction Materials
51. Electronic and Photonic Materials
51.1 Semiconductor Device Simulation
51.1.1 Transistor Design
51.1.2 Optoelectronic Device Modelling
52. Superconductors and Magnetic Materials
52.1 High-Temperature Superconductors
52.2 Magnetic Data Storage Materials
53. Energy Materials
53.1 Fuel Cell and Battery Materials
53.1.1 Lithium-Ion Batteries
53.1.2 Fuel Cell Catalysts
53.2 Solar Cell Materials
53.2.1 Photovoltaic Device Optimization
53.2.2 Organic Solar Cells
54. Nanomaterials and Nanotechnology
54.1 Modeling at the Nanoscale
54.2 Nanoparticle Synthesis and Properties
54.3 Nanocomposite Materials
Part 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation
55. Introduction to Quantum Mechanics
55.1 Wave Functions, Probability Densities and Operators
55.2 Postulates of Quantum Mechanics
55.3 The Time-Independent Schrödinger Equation
56. Molecular Hamiltonians and Operators
56.1 Born-Oppenheimer Approximation
56.2 Hamiltonian Operators
56.3 Expectation Values and Observables
57. Basis Sets and Wave Function Expansions
57.1 Atomic Orbitals and Basis Functions
57.2 Gaussian Basis Sets
57.3 Slater-Type Orbitals (STOs)
57.4 Types of Basis Sets
57.5 Plane Waves and Fourier Transforms
58. Introduction to Ab Initio Calculations
58.1 Hartree-Fock Theory
58.2 Configuration Interaction (CI)
58.3 Many-Body Perturbation Theory (MBPT)
59. Coupled Cluster Theory
59.1 Cluster Operators and Excitations
59.2 Single and Double Excitations (CCSD)
59.3 Higher-Order Excitations (CCSD(T))
60. Density Functional Theory (DFT)
60.1 Hohenberg−Kohn Theorem
60.2 Kohn-Sham Equations
60.3 Local Density Approximation (LDA)
60.4 Generalized Gradient Approximation (GGA)
60.5 Dispersion Corrected DFT
60.6 Hybrid Functional Theory
61. Advanced Topics in DFT
61.1 Time-Dependent DFT (TDDFT)
61.2 Linear Response and Excitation Energies
61.3 Optical Properties and Spectroscopy
61.4 Beyond TDDFT: Nonlinear Response
62. DFT for Strongly Correlated Systems
62.1 Hubbard U and DFT+U
62.2 Dynamical Mean Field Theory (DMFT)
62.3 Quantum Monte Carlo and DFT+QMC
63. Molecular Dynamics (MD) Simulation
63.1 Introduction
63.2 MD Using Simple Models
63.3 MD with Continuous Potentials
63.4 MD at Constant Temperatures and Pressures
63.5 MD with Solvent Effects: Mean Force and Stochastic Dynamics
63.6 Conformational Changes Post MD Simulation
64. Quantum Mechanics/Molecular Mechanics (QM/MM)
64.1 Hybrid Methods Overview
64.2 Implementation and Applications
64.3 Challenges and Considerations
65. Advanced MD Techniques
65.1 Umbrella Sampling
65.2 Metadynamics
65.3 Replica Exchange Molecular Dynamics (REMD)
65.4 Ab Initio Molecular Dynamics (AIMD)
66. Machine Learning in MD
66.1 Force Field Parametrization
66.2 Enhanced Sampling with ML
66.3 Other Important Methods
67. Simulation of Biomolecular Complexes
67.1 Protein-Ligand Complexes
67.2 Protein-Nucleic Acid Interactions
67.3 Protein-Protein Interactions: Large Protein Assemblies and Their Interactions
67.4 Membrane Proteins and Lipid Bilayers: Proteins Embedded in Lipid Membranes
Part 11. The Chemical Space
68. The Concept of Chemical Space
69. Importance in Chemistry and Beyond
70. The Chemical Spaces
70.1 Chemical Space for Pharmacy
70.2 Pharmacophore Space
70.3 Polypharmacology
70.4 Chemical Space for Natural Products
70.5 Chemical Space for Biology and Medicine
71. Docking for Virtual Screening of Chemical Space
71.1 Different Open-Source Docking
71.2 ML and DL Docking Tools for Chemical Space
72. Chemoinformatic Resources for Chemical Space
72.1 Various Chemical Space Databases
72.2 Open-Source Platforms for Chemoinformatics
72.3 Online Tools Developed for Mining Chemical and Target Spaces
72.4 Useful Servers for Mining Chemical and Target Spaces of Target Families or Diseases
73. Dimensions of Chemical Space
73.1 Structural-Based Dimensions
73.2 Descriptor-Based Dimensions
74. Advanced Approaches to Explore the Chemical Space
75. AI-ML Techniques and Tools for Chemical Space
Part 12. Generative Models for Novel Catalyst Design
76. Introduction
77. Foundations of Catalyst Design
77.1 Background and Significance of Catalyst Design
77.2 The Evolution of Catalyst Development Approaches
77.3 Role of Generative Models in Accelerating Catalyst Innovation
77.4 Traditional Catalyst Discovery Methods and Limitations
77.5 Need for Accelerated and Targeted Catalyst Design
78. Generative Models in Chemistry
78.1 Overview of Generative Models
78.2 Machine Learning and Deep Learning in Chemistry
78.3 Applications of Generative Models in Catalyst Design
79. Types of Generative Models
79.1 Variational Autoencoders (VAEs)
79.2 Generative Adversarial Networks (GANs)
79.3 Reinforcement Learning in Catalyst Design
79.4 Comparative Analysis of Generative Model Types
80. Catalyst Property Prediction
80.1 Predictive Modeling for Catalyst Activity and Selectivity
80.2 Quantitative Structure-Activity Relationship (QSAR) Models
80.3 Challenges and Opportunities in Property Prediction
81. Molecular Representation and Embedding
81.1 Encoding Chemical Structures for Generative Models
81.2 Graph Neural Networks (GNNs) in Catalyst Design
81.3 Embedding Techniques for Catalyst Descriptor Generation
82 Challenges and Considerations
82.1 Data Quality and Bias in Training Datasets
82.2 Transferability and Generalization of Generative Models
82.3 Ethical Considerations in AI-Driven Catalyst Design
83. Future Directions and Emerging Technologies
83.1 Advanced Generative Models on the Horizon
83.2 Integration with High-Throughput Experimentation
83.3 Collaborative Approaches in Catalyst Design
Part 13. Transforming Petroleum and Polymers Industry with AI
84. Petrochemicals as Sustainable Materials for the Modern World
85. Polymers
85.1 Polymerization Process
85.2 Polymer Synthesis from Petrochemicals
85.3 Polymer Characterization
85.4 Role of Catalyst in Polymerization
86. Advanced Polymer Materials
86.1 High-Performance Polymers for Specialized Applications
86.2 Bio-Based and Sustainable Polymers
86.3 Polymer Composites for Enhanced Properties
87. Applying Machine Learning for Polymer Research
87.1 Polymer Representations
87.2 Generating New Polymer Chemistries
87.3 Prediction of Properties for Sequence Defined Polymers
87.4 Polymer Composites for Enhanced Properties
87.5 Autonomous Experimentation
88. Membrane Design for Petroleum Research
88.1 Membrane Materials
88.2 Membrane Types and Usage
88.3 Revolutionizing Membrane Design Using Machine Learning
88.4 Topology Optimization
88.5 Predictive Models for Water Permeability and Salt Rejection
89. Interpretable Discovery of Innovative Polymers and Membranes with AI
Part 14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery
90. Introduction to Natural Products Drug Discovery
90.1 Overview of Traditional Drug Discovery Methods
90.2 Importance of Natural Products in Drug Development
90.3 Challenges in Natural Products Drug Discovery
91. Data Integration and Analysis
91.1 Integration of Biological and Chemical Data
91.2 Computational Approaches for Natural Products Screening
91.3 Data Mining Techniques in Drug Discovery
92. Predictive Modeling in Natural Products Research
92.1 Predictive Modeling for Bioactivity
92.2 Structure-Activity Relationship (SAR) Analysis
92.3 QSAR (Quantitative Structure-Activity Relationship) Models
93. Target Identification and Validation
93.1 Identification of Drug Targets Using AI
93.2 Validation of Targets in Natural Products Drug Discovery
93.3 Challenges and Solutions in Target Identification
94. Database Development for Natural Products
94.1 Necessity for the Natural Products Databases
94.2 NEIMPDB and OSADHI
95. Prediction of Targets and Biological Activity of Natural Products
96. Visualizing and Navigating the Natural Products Space in Chemical Space
97. The Natural Product Database Landscape
Part 15. Applying Machine Learning in Drug Repurposing
98. Drug Repurposing
98.1 Overview of Traditional Drug Development
98.2 Rationale for Drug Repurposing
99. Role of Machine Learning and Artificial Intelligence in Drug Repurposing
99.1 Evolution of AI in Drug Discovery
99.2 Machine Learning Approaches in Repurposing
99.3 Applications of AI in Identifying Repurposable Drugs
99.4 Data Integration and Analysis for Drug Repurposing
100. Integration of Biomedical Data Sources
100.1 Computational Approaches for Data Analysis
100.2 Utilizing Real-World Evidence (RWE) in Repurposing
101. Network Pharmacology and Drug Repurposing
101.1 Network-Based Approaches to Drug Repurposing
101.2 Analysis of Biological Pathways and Networks
101.3 Predictive Modeling in Network Pharmacology
102. Predictive Analytics for Drug Repurposing
102.1 Predictive Modeling Techniques
102.2 Machine Learning Algorithms for Prediction
102.3 Quantitative Structure-Activity Relationship (QSAR) in Repurposing
103. High-Throughput Screening and Virtual Screening in Drug Repurposing
103.1 Automation in High-Throughput Screening
103.2 Virtual Screening Using AI Algorithms
103.3 Integration of Experimental and Computational Screening
104. Identification of Novel Targets for Drug Repurposing
104.1 Target Identification Using AI
104.2 Validation of Targets for Repurposing
104.3 Challenges and Strategies in Target Identification
105 Combination Therapy and Synergistic Drug Repurposing
105.1 AI-Guided Combination Therapy Strategies
105.2 Identifying Synergistic Drug Combinations
105.3 Challenges and Opportunities in Combination Repurposing
106. Ethical and Regulatory Considerations in Drug Repurposing
106.1 Ethical Issues in AI-Driven Drug Repurposing
106.2 Regulatory Compliance and Safety Assessment
106.3 Balancing Innovation with Ethical and Legal Frameworks
107. Implications for the Future of Drug Repurposing with AI
Part 16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry
108. Introduction
109. Fourth Industrial Revolution and the Rise of Industry 5.0
110. Role of Artificial Intelligence (AI) in Industry 5.0
111. Towards an AI-Assisted, Automated Chemistry Lab
111.1 Robotics and AI
111.2 Concept of the "Self-Driving" Lab
111.3 Remaining Hurdles to Realize the Vision of Industry 5.0
111.4 Will AI Ever Replace Human Chemical Intuition?
112. AI in Industry 5.0: Driving Smart Manufacturing
112.1 The Role of AI in Predictive Maintenance and Quality Control
112.2 AI-Powered Robotics and Automation
112.3 AI-Driven Supply Chain Optimization
112.4 AI-Driven Materials Databases and Repositories
113. Challenges and Opportunities of Industry 5.0
114. Future Trends in Reaction Modelling
114.1 Advances in Computational Approaches
114.2 Integration of Machine Learning and AI
114.3 Emerging Applications and Challenges
114.4 Importance of Reaction Modelling in Science and Industry
115. Machine Learning for Materials Simulation
115.1 High Throughput Virtual Screening
115.2 Analyzing Experimental Data
115.3 Active Learning for Materials
116. Various Tools for Computational Chemistry Using AI
117. Evolution of Chemical and Biological Space Using AI
118. Open-Source Tools of Computational Chemistry Using AI, ML, and DL
119. Latest Interventions of AI in Computational Chemistry
120. Future Prospects
Product details
Product details
- Edition: 1
- Latest edition
- Published: February 1, 2027
- Language: English
About the editors
About the editors
GS
Garikapati Narahari Sastry
Garikapati Narahari Sastry is currently working as the Director of CSIR-North East Institute of Science and Technology, Jorhat. Prof. Sastry is a chemist, working in the interdisciplinary areas spanning chemistry, biology, modeling, and informatics. Prof. Sastry has been effective in employing computational and theoretical methods to solve problems in chemistry, biology, and allied areas. These efforts are embedded not only to provide a robust platform for carrying out research in CADD but also to inculcate the culture of developing software packages. He has made fundamental contributions in the areas of a) computational and theoretical chemistry; b) theoretical organic chemistry and reaction mechanism; c) software and data base development for drug discovery (Molecular Property Diagnostic Suite), d) non-covalent interactions, e) cooperativity of non-covalent interactions, f) computer-aided drug design. Under his guidance 29 people were awarded Ph.D., 20 Post-doctoral fellows, 235 students have done internship or short-term projects. In his career, he has delivered more than 480 lectures in international and national conferences/workshops/seminars. His research work was published in more than 330 research papers and reviews, which received over 12,554 citations, with an h-index of 55.
Affiliations and expertise
Director, CSIR – North East Institute of Science and Technology, Jorhat, Assam, IndiaHM
Hridoy Jyoti Mahanta
Hridoy Jyoti Mahanta obtained a PhD in Computer Science and Engineering from Assam University, Silchar, India. He is currently working as a Scientist in the Advanced Computation and Data Sciences Division at CSIR-North East Institute of Science and Technology, Jorhat. His areas of interest include Artificial Intelligence, Machine Learning, Deep Learning, and their applications in the natural sciences, database development, and Software development. He has been closely working with Dr. G. Narahari Sastry for the past three and a half years, focusing on applying artificial intelligence and machine learning to solve fundamental problems in Bioinformatics, Chemoinformatics, and Chemistry. He has published around 36 papers in peer-reviewed journals and international conferences.
Affiliations and expertise
Advanced Computation and Data Sciences Division at CSIR-North East Institute of Science and Technology, Jorhat. IndiaSN
Selvaraman Nagamani
Selvaraman Nagamani is a Scientist at Advanced Computation and Data Sciences Division, CSIR – North East Institute of Science and Technology, Jorhat, Assam, India. He obtained his PhD from Alagappa University, India and received the ICMR – Senior Research Fellowship (2014-2016). In 2017, he received prestigious DST – National Postdoctoral Fellowship to work with Dr. G. Narahari Sastry in CSIR – IICT Hyderabad. In 2021, he joined as a Scientist in Advanced Computation and Data Sciences Division, CSIR – NEIST. His research interests are developing open-source computational drug discovery software, applying novel and state-of-the-art computer aided drug design methods, network pharmacology, AI, and ML approaches in computational drug discovery. He has published more than 50 papers in peer review journals.
Affiliations and expertise
Advanced Computation and Data Sciences Division, CSIR – North East Institute of Science and Technology, IndiaDT
Dinadayalane Tandabany
Dinadayalane Tandabany has been Associate Professor of Chemistry at Clark Atlanta University, USA, since 2014. After being awarded his Ph.D in Chemistry from Pondicherry University, India, he took up a research position at Jackson State University, USA, where he conducted high performance computational investigations of structures, reactivities, electronic, transport and mechanical properties of carbon based nanomaterials, and taught a number of classes in general and computational chemistry prior to taking up his current role.
He has co-authored over 70 papers and 8 book chapters, has been awarded a number of awards for his work, and has presented talks at numerous conferences. In addition, he actively works to help increase the number of underrepresented undergraduate and graduate students in computational chemistry and nanoscience research.
Affiliations and expertise
Associate Professor, Clark Atlanta University, Atlanta, Georgia, USA