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.

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• Part 1. Artificial Intelligence1. A Comprehensive Introduction to AI2. Chemical Space and AI3. Impact of AI in Computational Chemistry4. Machine Learning Applications in Computational Chemistry5. AI-Driven Approaches in Quantum Chemistry6. Future and Challenges of AI in ChemistryPart 2. Machine Learning7. Fundamental Concepts of Machine Learning8. Understanding the Foundations9. Essential Steps in Applying Machine Learning10. Machine Learning in Various Fields of Natural Sciences11. From Machine Learning to Deep Learning12. Rise of Generative Models and Industry 5.0Part 3. Scientific Computing Using Python13. Python Basics14. Handling Numeric Data with NumPy15. Utilities of Pandas16. Visualization with Matplotlib and Seaborn17. RdKit for Chemoinformatics18. Chemypy PackagePart 4. Machine Learning with Python19. Scikit-learn Library in Python20. Data Representation and Generation21. Supervised Machine Learning22. Unsupervised Machine Learning23. Evaluation Metrics24. Case StudiesPart 5. Evolution of Computational Chemistry25. Overview of Computational Chemistry26. Era of High-Performance Computing27. Software and Tools28. Recent Advances and Future DirectionsPart 6. Structure-Property Relationships29. Fundamentals of Structure-Property Relationships30. Chemical Structure and Property Correlations31. Quantitative Structure-Property Relationships (QSPR)32. Quantitative Structure-Activity Relationships (QSAR)33. Materials Science and Structure-Property Relationships34. Biological Systems and Structure-Property RelationshipsPart 7. Reaction Modelling35. Overview of Reaction Modelling36. Chemical Kinetics37. Reaction Mechanisms38. Reaction Rate Constants39. Reaction Modelling Approaches40. Numerical Methods for Reaction ModellingPart 8. Computer-Aided Drug Design41. Introduction to Computer-Aided Materials (Drug) Design43. Molecular Modeling in Drug Design44. Virtual Screening and Compound Selection45. De Novo Drug Discovery46. Chemoinformatics and Bioinformatics47. ADME/Toxicity PredictionPart 9. Materials Modelling48. Introduction49. Materials50. Material Design for Specific Applications51. Electronic and Photonic Materials52. Superconductors and Magnetic Materials53. Energy Materials54. Nanomaterials and NanotechnologyPart 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation55. Introduction to Quantum Mechanics56. Molecular Hamiltonians and Operators57. Basis Sets and Wave Function Expansions58. Introduction to Ab Initio Calculations59. Coupled Cluster Theory60. Density Functional Theory (DFT)61. Advanced Topics in DFT62. DFT for Strongly Correlated Systems63. Molecular Dynamics (MD) Simulation64. Quantum Mechanics/Molecular Mechanics (QM/MM)65. Advanced MD Techniques65.4 Ab Initio Molecular Dynamics (AIMD)67. Simulation of Biomolecular ComplexesPart 11. The Chemical Space68. The Concept of Chemical Space69. Importance in Chemistry and Beyond70. The Chemical Spaces71. Docking for Virtual Screening of Chemical Space73. Dimensions of Chemical Space74. Advanced Approaches to Explore the Chemical Space75. AI-ML Techniques and Tools for Chemical SpacePart 12. Generative Models for Novel Catalyst Design76. Introduction77. Foundations of Catalyst Design78. Generative Models in Chemistry79. Types of Generative Models80. Catalyst Property Prediction81. Molecular Representation and Embedding82 Challenges and Considerations83. Future Directions and Emerging TechnologiesPart 13. Transforming Petroleum and Polymers Industry with AI84. Petrochemicals as Sustainable Materials for the Modern World85. Polymers86. Advanced Polymer Materials87. Applying Machine Learning for Polymer Research88. Membrane Design for Petroleum Research89. Interpretable Discovery of Innovative Polymers and Membranes with AIPart 14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery90. Introduction to Natural Products Drug Discovery91. Data Integration and Analysis92. Predictive Modeling in Natural Products Research93. Target Identification and Validation94. Database Development for Natural Products95. Prediction of Targets and Biological Activity of Natural Products96. Visualizing and Navigating the Natural Products Space in Chemical Space97. The Natural Product Database LandscapePart 15. Applying Machine Learning in Drug Repurposing98. Drug Repurposing99. Role of Machine Learning and Artificial Intelligence in Drug Repurposing100. Integration of Biomedical Data Sources101. Network Pharmacology and Drug Repurposing102. Predictive Analytics for Drug Repurposing103. High-Throughput Screening and Virtual Screening in Drug Repurposing104. Identification of Novel Targets for Drug Repurposing105 Combination Therapy and Synergistic Drug Repurposing106. Ethical and Regulatory Considerations in Drug Repurposing107. Implications for the Future of Drug Repurposing with AIPart 16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry108. Introduction109. Fourth Industrial Revolution and the Rise of Industry 5.0110. Role of Artificial Intelligence (AI) in Industry 5.0111. Towards an AI-Assisted, Automated Chemistry Lab112. AI in Industry 5.0: Driving Smart Manufacturing113. Challenges and Opportunities of Industry 5.0114. Future Trends in Reaction Modelling115. Machine Learning for Materials Simulation116. Various Tools for Computational Chemistry Using AI117. Evolution of Chemical and Biological Space Using AI118. Open-Source Tools of Computational Chemistry Using AI, ML, and DL119. Latest Interventions of AI in Computational Chemistry120. Future Prospects

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.

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• Part 1. Artificial Intelligence1. A Comprehensive Introduction to AI2. Chemical Space and AI3. Impact of AI in Computational Chemistry4. Machine Learning Applications in Computational Chemistry5. AI-Driven Approaches in Quantum Chemistry6. Future and Challenges of AI in ChemistryPart 2. Machine Learning7. Fundamental Concepts of Machine Learning8. Understanding the Foundations9. Essential Steps in Applying Machine Learning10. Machine Learning in Various Fields of Natural Sciences11. From Machine Learning to Deep Learning12. Rise of Generative Models and Industry 5.0Part 3. Scientific Computing Using Python13. Python Basics14. Handling Numeric Data with NumPy15. Utilities of Pandas16. Visualization with Matplotlib and Seaborn17. RdKit for Chemoinformatics18. Chemypy PackagePart 4. Machine Learning with Python19. Scikit-learn Library in Python20. Data Representation and Generation21. Supervised Machine Learning22. Unsupervised Machine Learning23. Evaluation Metrics24. Case StudiesPart 5. Evolution of Computational Chemistry25. Overview of Computational Chemistry26. Era of High-Performance Computing27. Software and Tools28. Recent Advances and Future DirectionsPart 6. Structure-Property Relationships29. Fundamentals of Structure-Property Relationships30. Chemical Structure and Property Correlations31. Quantitative Structure-Property Relationships (QSPR)32. Quantitative Structure-Activity Relationships (QSAR)33. Materials Science and Structure-Property Relationships34. Biological Systems and Structure-Property RelationshipsPart 7. Reaction Modelling35. Overview of Reaction Modelling36. Chemical Kinetics37. Reaction Mechanisms38. Reaction Rate Constants39. Reaction Modelling Approaches40. Numerical Methods for Reaction ModellingPart 8. Computer-Aided Drug Design41. Introduction to Computer-Aided Materials (Drug) Design43. Molecular Modeling in Drug Design44. Virtual Screening and Compound Selection45. De Novo Drug Discovery46. Chemoinformatics and Bioinformatics47. ADME/Toxicity PredictionPart 9. Materials Modelling48. Introduction49. Materials50. Material Design for Specific Applications51. Electronic and Photonic Materials52. Superconductors and Magnetic Materials53. Energy Materials54. Nanomaterials and NanotechnologyPart 10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation55. Introduction to Quantum Mechanics56. Molecular Hamiltonians and Operators57. Basis Sets and Wave Function Expansions58. Introduction to Ab Initio Calculations59. Coupled Cluster Theory60. Density Functional Theory (DFT)61. Advanced Topics in DFT62. DFT for Strongly Correlated Systems63. Molecular Dynamics (MD) Simulation64. Quantum Mechanics/Molecular Mechanics (QM/MM)65. Advanced MD Techniques65.4 Ab Initio Molecular Dynamics (AIMD)67. Simulation of Biomolecular ComplexesPart 11. The Chemical Space68. The Concept of Chemical Space69. Importance in Chemistry and Beyond70. The Chemical Spaces71. Docking for Virtual Screening of Chemical Space73. Dimensions of Chemical Space74. Advanced Approaches to Explore the Chemical Space75. AI-ML Techniques and Tools for Chemical SpacePart 12. Generative Models for Novel Catalyst Design76. Introduction77. Foundations of Catalyst Design78. Generative Models in Chemistry79. Types of Generative Models80. Catalyst Property Prediction81. Molecular Representation and Embedding82 Challenges and Considerations83. Future Directions and Emerging TechnologiesPart 13. Transforming Petroleum and Polymers Industry with AI84. Petrochemicals as Sustainable Materials for the Modern World85. Polymers86. Advanced Polymer Materials87. Applying Machine Learning for Polymer Research88. Membrane Design for Petroleum Research89. Interpretable Discovery of Innovative Polymers and Membranes with AIPart 14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery90. Introduction to Natural Products Drug Discovery91. Data Integration and Analysis92. Predictive Modeling in Natural Products Research93. Target Identification and Validation94. Database Development for Natural Products95. Prediction of Targets and Biological Activity of Natural Products96. Visualizing and Navigating the Natural Products Space in Chemical Space97. The Natural Product Database LandscapePart 15. Applying Machine Learning in Drug Repurposing98. Drug Repurposing99. Role of Machine Learning and Artificial Intelligence in Drug Repurposing100. Integration of Biomedical Data Sources101. Network Pharmacology and Drug Repurposing102. Predictive Analytics for Drug Repurposing103. High-Throughput Screening and Virtual Screening in Drug Repurposing104. Identification of Novel Targets for Drug Repurposing105 Combination Therapy and Synergistic Drug Repurposing106. Ethical and Regulatory Considerations in Drug Repurposing107. Implications for the Future of Drug Repurposing with AIPart 16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry108. Introduction109. Fourth Industrial Revolution and the Rise of Industry 5.0110. Role of Artificial Intelligence (AI) in Industry 5.0111. Towards an AI-Assisted, Automated Chemistry Lab112. AI in Industry 5.0: Driving Smart Manufacturing113. Challenges and Opportunities of Industry 5.0114. Future Trends in Reaction Modelling115. Machine Learning for Materials Simulation116. Various Tools for Computational Chemistry Using AI117. Evolution of Chemical and Biological Space Using AI118. Open-Source Tools of Computational Chemistry Using AI, ML, and DL119. Latest Interventions of AI in Computational Chemistry120. Future Prospects