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Sastry / Mahanta / Nagamani

Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0

Medium: Buch
ISBN: 978-0-443-33714-7
Verlag: Elsevier Science
Erscheinungstermin: 01.02.2027
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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.

Produkteigenschaften

  • Artikelnummer: 9780443337147
  • Medium: Buch
  • ISBN: 978-0-443-33714-7
  • Verlag: Elsevier Science
  • Erscheinungstermin: 01.02.2027
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2027
  • Produktform: Kartoniert
  • Seiten: 416
  • Format (B x H): 191 x 235 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Herausgeber

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.

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.

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.

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.

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
5. AI-Driven Approaches in Quantum Chemistry
6. Future and Challenges of AI in Chemistry

Part 2. Machine Learning
7. Fundamental Concepts of Machine Learning
8. Understanding the Foundations
9. Essential Steps in Applying Machine Learning
10. Machine Learning in Various Fields of Natural 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
14. Handling Numeric Data with NumPy
15. Utilities of Pandas
16. Visualization with Matplotlib and Seaborn
17. RdKit for Chemoinformatics
18. Chemypy Package

Part 4. Machine Learning with Python
19. Scikit-learn Library in Python
20. Data Representation and Generation
21. Supervised Machine Learning
22. Unsupervised Machine Learning
23. Evaluation Metrics
24. Case Studies

Part 5. Evolution of Computational Chemistry
25. Overview of Computational Chemistry
26. Era of High-Performance Computing
27. Software and Tools
28. Recent Advances and Future Directions

Part 6. Structure-Property Relationships
29. Fundamentals of Structure-Property Relationships
30. Chemical Structure and Property Correlations
31. Quantitative Structure-Property Relationships (QSPR)
32. Quantitative Structure-Activity Relationships (QSAR)
33. Materials Science and Structure-Property Relationships
34. Biological Systems and Structure-Property Relationships

Part 7. Reaction Modelling
35. Overview of Reaction Modelling
36. Chemical Kinetics
37. Reaction Mechanisms
38. Reaction Rate Constants
39. Reaction Modelling Approaches
40. Numerical Methods for Reaction Modelling

Part 8. Computer-Aided Drug Design
41. Introduction to Computer-Aided Materials (Drug) Design
43. Molecular Modeling in Drug Design
44. Virtual Screening and Compound Selection
45. De Novo Drug Discovery
46. Chemoinformatics and Bioinformatics
47. ADME/Toxicity Prediction

Part 9. Materials Modelling
48. Introduction
49. Materials
50. Material Design for Specific Applications
51. Electronic and Photonic Materials
52. Superconductors and Magnetic Materials
53. Energy Materials
54. Nanomaterials and Nanotechnology

Part 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation
55. Introduction to Quantum Mechanics
56. Molecular Hamiltonians and Operators
57. Basis Sets and Wave Function Expansions
58. Introduction to Ab Initio Calculations
59. Coupled Cluster Theory
60. Density Functional Theory (DFT)
61. Advanced Topics in DFT
62. DFT for Strongly Correlated Systems
63. Molecular Dynamics (MD) Simulation
64. Quantum Mechanics/Molecular Mechanics (QM/MM)
65. Advanced MD Techniques
65.4 Ab Initio Molecular Dynamics (AIMD)
67. Simulation of Biomolecular Complexes

Part 11. The Chemical Space
68. The Concept of Chemical Space
69. Importance in Chemistry and Beyond
70. The Chemical Spaces
71. Docking for Virtual Screening of Chemical Space
73. Dimensions of Chemical Space
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
78. Generative Models in Chemistry
79. Types of Generative Models
80. Catalyst Property Prediction
81. Molecular Representation and Embedding
82 Challenges and Considerations
83. Future Directions and Emerging Technologies

Part 13. Transforming Petroleum and Polymers Industry with AI
84. Petrochemicals as Sustainable Materials for the Modern World
85. Polymers
86. Advanced Polymer Materials
87. Applying Machine Learning for Polymer Research
88. Membrane Design for Petroleum Research
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
91. Data Integration and Analysis
92. Predictive Modeling in Natural Products Research
93. Target Identification and Validation
94. Database Development for Natural Products
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
99. Role of Machine Learning and Artificial Intelligence in Drug Repurposing
100. Integration of Biomedical Data Sources
101. Network Pharmacology and Drug Repurposing
102. Predictive Analytics for Drug Repurposing
103. High-Throughput Screening and Virtual Screening in Drug Repurposing
104. Identification of Novel Targets for Drug Repurposing
105 Combination Therapy and Synergistic Drug Repurposing
106. Ethical and Regulatory Considerations in Drug Repurposing
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
112. AI in Industry 5.0: Driving Smart Manufacturing
113. Challenges and Opportunities of Industry 5.0
114. Future Trends in Reaction Modelling
115. Machine Learning for Materials Simulation
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