Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0
- 1st Edition - August 1, 2026
- 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
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.
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.
- 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
Graduate and postgraduate students and their tutors focused on computational chemistry and cheminformatics
1. Artificial Intelligence
1.1 A Comprehensive Introduction to AI
1.2 Chemical Space and AI
1.3 Impact of AI in Computational Chemistry
1.4 Machine Learning Applications in Computational Chemistry
1.4.1 ML and QSAR
1.4.2 Property Prediction
1.4.3 Generative Models for Molecular Design
1.4.4 Chemical Reactions
1.5 AI-Driven Approaches in Quantum Chemistry
1.5.1 Quantum Property Prediction
1.5.2 Quantum Circuit Optimization
1.5.3 Quantum Machine Learning for Molecular Systems
1.6 Future and Challenges of AI in Chemistry
Conclusion
References
2. Machine Learning
2.1 Fundamental Concepts of Machine Learning
2.2 Understanding the Foundations
2.2.1 Human Learning and Types of Human Learning
2.2.2 Human versus Machine Learning
2.2.3 Supervised and Unsupervised Learning
2.2.4 Neural Networks and Deep Learning
2.2.5 Generative Learning
2.3 Essential Steps in Applying Machine Learning
2.3.1 Preparation and Handling of Data
2.3.2 Feature Engineering and Feature Selection
2.3.3 Model Building and Validation
2.3.4 Evaluation
2.3.5 Applicability Domain and Deployment
2.4 Machine Learning in Various Fields of Natural Sciences
2.4.1 Material Sciences
2.4.2 Chemical Sciences
2.4.3 Life Sciences
2.4.4 Environmental Sciences
2.4.5 Agricultural Sciences
2.5 From Machine Learning to Deep Learning
2.6 Rise of Generative Models and Industry 5.0
Conclusion
References
3. Scientific Computing Using Python
3.1 Python Basics
3.1.1 Creating Python Environment
3.1.2 Installation of Packages and Libraries
3.1.3 Python Workbenches
3.2 Handling Numeric Data with NumPy
3.2.1 Basic Computing Operations
3.2.2 Arrays and Indexing
3.2.3 Vectorization
3.2.4 Functions and Methods
3.2.5 Dealing with Missing Data
3.2.6 Generating Random Numbers
3.3 Utilities of Pandas
3.3.1 Reading and Handling Data with Pandas
3.3.2 Selecting and Indexing
3.3.3 Advanced Indexing
3.3.4 Handling Text Data
3.3.5 Statistical Functions with Pandas
3.4 Visualization with Matplotlib and Seaborn
3.4.1 Plotting Basics
3.4.2 Various Types of Plots in Matplotlib
3.4.3 Overlaying and Multifigure Plots
3.4.4 3-Dimensional Plotting
3.4.5 Seaborn Plot Types
3.4.6 Categorical Plots
3.4.7 Distribution and Pair Plots
3.4.8 Correlation and Heatmaps
3.5 RdKit for Chemoinformatics
3.5.1 Molecule Representations
3.5.2 Visualizing Structures
3.5.3 Basic Operations with RdKit
3.5.4 Finding Descriptors
3.5.5 Atoms and Bonds
3.5.6 Similarity and Searching Patterns
3.6 Chemypy Package
3.6.1 Basics and Installation
3.6.2 Handling Reactions
3.6.3 Chemical Kinetics
3.6.4 Finding Properties
3.6.5 Other Utilities
Conclusion
References
4. Machine Learning with Python
4.1 Scikit-learn Library in Python
4.2 Data Representation and Generation
4.2.1 Generating Synthetic Data
4.2.2 Exploring Data Sets
4.2.3 Data Preprocessing and Preparation
4.2.4 Feature Engineering
4.3 Supervised Machine Learning
4.3.1 Training for Linear Regression
4.3.2 Multi-Linear Regression
4.3.3 Classification with Logistic Regression
4.3.4 Random Forest-Based Classification
4.4 Unsupervised Machine Learning
4.4.1 Dimensionality Reduction
4.4.2 Clustering with Partitioned Algorithms
4.4.3 Hierarchical Clustering
4.5 Evaluation Metrics
4.5.1 Confusion Matrix
4.5.2 Accuracy and Error
4.5.3 Precision and Recall
4.5.4 ROC-AUC
4.5.5 Cluster Analysis Metrics
4.6 Case Studies
Conclusion
References
5. Evolution of Computational Chemistry
5.1 Overview of Computational Chemistry
5.1.1 Computational Tools and Techniques
5.1.2 Significance and Contributions
5.2 Era of High-Performance Computing
5.2.1 Role of Supercomputing in Computational Chemistry
5.2.2 Parallelization and Acceleration Techniques
5.2.3 Cloud Computing and Distributed Computing
5.3 Software and Tools
5.3.1 Overview of Computational Chemistry Software
5.3.2 Open-Source vs. Commercial Software
5.3.3 Popular Software Packages and Their Capabilities
5.3.4 Intersection of Machine Learning and Computational Chemistry
5.3.5 Predictive Modelling and Property Estimation
5.4 Recent Advances and Future Directions
5.4.1 Quantum Computing and Its Impact
5.4.2 Multiscale Modelling and Simulation
5.4.3 Emerging Fields and Interdisciplinary Applications
Conclusion
References
6. Structure-Property Relationships
6.1 Fundamentals of Structure-Property Relationships
6.1.1 Defining Structure and Property
6.1.2 Understanding Structure-Property Relationships
6.1.3 Interlinking Molecular/Structural Features and Properties
6.2 Chemical Structure and Property Correlations
6.2.1 Molecular Structure and Properties
6.2.2 Electronic Structure and Optical Properties
6.2.3 Topological and Geometrical Descriptors
6.3 Quantitative Structure-Property Relationships (QSPR)
6.3.1 Developing QSPR Models
6.3.2 Regression Analysis and Parameterization
6.3.3 Applicability and Limitations
6.4 Quantitative Structure-Activity Relationships (QSAR)
6.4.1 QSAR in Drug Design
6.4.2 Molecular Descriptors in QSAR
6.4.3 Predictive Modelling and Toxicology
6.5 Materials Science and Structure-Property Relationships
6.5.1 Atomic and Crystal Structures
6.5.2 Mechanical Properties of Materials
6.5.3 Thermodynamic and Electronic Properties
6.6 Biological Systems and Structure-Property Relationships
6.6.1 Proteins and Enzymes
6.6.2 DNA and RNA
6.6.3 Structure-Function Relationships in Biology
Conclusion
References
7. Reaction Modelling
7.1 Overview of Reaction Modelling
7.2 Chemical Kinetics
7.2.1 Basics of Chemical Reactions
7.2.2 Reaction Rate and Rate Laws
7.2.3 Factors Affecting Reaction Rates
7.3 Reaction Mechanisms
7.3.1 Elementary Reactions vs. Overall Reactions
7.3.2 Reaction Intermediates
7.3.3 Reaction Mechanism Determination
7.3.4 Analysis of Reaction Potential Energy Surface
7.4 Reaction Rate Constants
7.4.1 Arrhenius Equation
7.4.2 Temperature Dependence
7.4.3 Catalysis and Reaction Rate Constants
7.5 Reaction Modelling Approaches
7.5.1 Homogeneous vs. Heterogeneous Reactions
7.5.2 Batch, Plug Flow, and Continuous Stirred Tank Reactors
7.5.3 Ideal vs. Non-Ideal Reactors
7.6 Numerical Methods for Reaction Modelling
7.6.1 Finite Difference Methods
7.6.2 Finite Element Methods
7.6.3 Computational Fluid Dynamics (CFD)
Conclusion
References
8. Computer-Aided Drug Design
8.1 Introduction to Computer-Aided Materials (Drug) Design
8.2 Drug Discovery Process
8.2.1 Target Identification and Validation
8.2.2 High-Throughput Screening (HTS)
8.2.3 Hit-to-Lead Optimization
8.2.4 Lead Optimization and Preclinical Testing
8.3 Molecular Modeling in Drug Design
8.3.1 Protein Structure Prediction
8.3.2 Ligand Docking and Binding Affinity Prediction
8.3.3 Pharmacophore Modeling
8.3.4 Quantitative Structure-Activity Relationship (QSAR) Studies
8.4 Virtual Screening and Compound Selection
8.4.1 Structure-Based Virtual Screening
8.4.2 Ligand-Based Virtual Screening
8.4.3 Fragment-Based Drug Design
8.5 De Novo Drug Discovery
8.5.1 De Novo Molecular Design
8.5.2 Computer-Generated Molecule Libraries
8.5.3 Optimization Algorithms in Rational Drug Design
8.6 Chemoinformatics and Bioinformatics
8.6.1 Molecular Databases and Data Mining
8.6.2 Sequence Analysis in Drug Discovery
8.6.3 Chemoinformatics for Compound Analysis
8.7 ADME/Toxicity Prediction
8.7.1 Absorption, Distribution, Metabolism, and Excretion (ADME)
8.7.2 Predicting Drug Toxicity
8.7.3 Risk Assessment in Drug Design
Conclusion
References
9. Materials Modelling
9.1 Introduction
9.1.1 Role of Materials Modelling in Science and Engineering
9.1.2 Overview of Computational Methods
9.2 Materials
9.2.1 Predicting Mechanical Properties
9.2.1.1 Strength and Elasticity
9.2.1.2 Ductility and Toughness
9.3 Material Design for Specific Applications
9.3.1 Aerospace Materials
9.3.2 Automotive Materials
9.3.3 Building and Construction Materials
9.4 Electronic and Photonic Materials
9.4.1 Semiconductor Device Simulation
9.4.1.1 Transistor Design
9.4.1.2 Optoelectronic Device Modelling
9.5 Superconductors and Magnetic Materials
9.5.1 High-Temperature Superconductors
9.5.2 Magnetic Data Storage Materials
9.6 Energy Materials
9.6.1 Fuel Cell and Battery Materials
9.6.1.1 Lithium-Ion Batteries
9.6.1.2 Fuel Cell Catalysts
9.6.2 Solar Cell Materials
9.6.2.1 Photovoltaic Device Optimization
9.6.2.2 Organic Solar Cells
9.7 Nanomaterials and Nanotechnology
9.7.1 Modeling at the Nanoscale
9.7.2 Nanoparticle Synthesis and Properties
9.7.3 Nanocomposite Materials
Conclusion
References
10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation
10.1 Introduction to Quantum Mechanics
10.1.1 Wave Functions, Probability Densities and Operators
10.1.2 Postulates of Quantum Mechanics
10.1.3 The Time-Independent Schrödinger Equation
10.2 Molecular Hamiltonians and Operators
10.2.1 Born-Oppenheimer Approximation
10.2.2 Hamiltonian Operators
10.2.3 Expectation Values and Observables
10.3 Basis Sets and Wave Function Expansions
10.3.1 Atomic Orbitals and Basis Functions
10.3.2 Gaussian Basis Sets
10.3.3 Slater-Type Orbitals (STOs)
10.3.4 Types of Basis Sets
10.3.5 Plane Waves and Fourier Transforms
10.4 Introduction to Ab Initio Calculations
10.4.1 Hartree-Fock Theory
10.4.2 Configuration Interaction (CI)
10.4.3 Many-Body Perturbation Theory (MBPT)
10.5 Coupled Cluster Theory
10.5.1 Cluster Operators and Excitations
10.5.2 Single and Double Excitations (CCSD)
10.5.3 Higher-Order Excitations (CCSD(T))
10.6 Density Functional Theory (DFT)
10.6.1 Hohenberg−Kohn Theorem
10.6.2 Kohn-Sham Equations
10.6.3 Local Density Approximation (LDA)
10.6.4 Generalized Gradient Approximation (GGA)
10.6.5 Dispersion Corrected DFT
10.6.6 Hybrid Functional Theory
10.7 Advanced Topics in DFT
10.7.1 Time-Dependent DFT (TDDFT)
10.7.2 Linear Response and Excitation Energies
10.7.3 Optical Properties and Spectroscopy
10.7.4 Beyond TDDFT: Nonlinear Response
10.8 DFT for Strongly Correlated Systems
10.8.1 Hubbard U and DFT+U
10.8.2 Dynamical Mean Field Theory (DMFT)
10.8.3 Quantum Monte Carlo and DFT+QMC
10.9 Molecular Dynamics (MD) Simulation
10.9.1 Introduction
10.9.2 MD Using Simple Models
10.9.3 MD with Continuous Potentials
10.9.4 MD at Constant Temperatures and Pressures
10.9.5 MD with Solvent Effects: Mean Force and Stochastic Dynamics
10.9.6 Conformational Changes Post MD Simulation
10.10 Quantum Mechanics/Molecular Mechanics (QM/MM)
10.10.1 Hybrid Methods Overview
10.10.2 Implementation and Applications
10.10.3 Challenges and Considerations
10.11 Advanced MD Techniques
10.11.1 Umbrella Sampling
10.11.2 Metadynamics
10.11.3 Replica Exchange Molecular Dynamics (REMD)
10.11.4 Ab Initio Molecular Dynamics (AIMD)
10.12 Machine Learning in MD
10.12.1 Force Field Parametrization
10.12.2 Enhanced Sampling with ML
10.12.3 Other Important Methods
10.13 Simulation of Biomolecular Complexes
10.13.1 Protein-Ligand Complexes
10.13.2 Protein-Nucleic Acid Interactions
10.13.3 Protein-Protein Interactions: Large Protein Assemblies and Their Interactions
10.13.4 Membrane Proteins and Lipid Bilayers: Proteins Embedded in Lipid Membranes
Conclusion
References
11. The Chemical Space
11.1 The Concept of Chemical Space
11.2 Importance in Chemistry and Beyond
11.3 The Chemical Spaces
11.3.1 Chemical Space for Pharmacy
11.3.2 Pharmacophore Space
11.3.3 Polypharmacology
11.3.4 Chemical Space for Natural Products
11.3.5 Chemical Space for Biology and Medicine
11.4 Docking for Virtual Screening of Chemical Space
11.4.1 Different Open-Source Docking
11.4.2 ML and DL Docking Tools for Chemical Space
11.5 Chemoinformatic Resources for Chemical Space
11.5.1 Various Chemical Space Databases
11.5.2 Open-Source Platforms for Chemoinformatics
11.5.3 Online Tools Developed for Mining Chemical and Target Spaces
11.5.4 Useful Servers for Mining Chemical and Target Spaces of Target Families or Diseases
11.6 Dimensions of Chemical Space
11.6.1 Structural-Based Dimensions
11.6.2 Descriptor-Based Dimensions
11.7 Advanced Approaches to Explore the Chemical Space
11.8 AI-ML Techniques and Tools for Chemical Space
Conclusion
References
12. Generative Models for Novel Catalyst Design
12.1 Introduction
12.2 Foundations of Catalyst Design
12.2.1 Background and Significance of Catalyst Design
12.2.2 The Evolution of Catalyst Development Approaches
12.2.3 Role of Generative Models in Accelerating Catalyst Innovation
12.2.4 Traditional Catalyst Discovery Methods and Limitations
12.2.5 Need for Accelerated and Targeted Catalyst Design
12.3 Generative Models in Chemistry
12.3.1 Overview of Generative Models
12.3.2 Machine Learning and Deep Learning in Chemistry
12.3.3 Applications of Generative Models in Catalyst Design
12.4 Types of Generative Models
12.4.1 Variational Autoencoders (VAEs)
12.4.2 Generative Adversarial Networks (GANs)
12.4.3 Reinforcement Learning in Catalyst Design
12.4.4 Comparative Analysis of Generative Model Types
12.5 Catalyst Property Prediction
12.5.1 Predictive Modeling for Catalyst Activity and Selectivity
12.5.2 Quantitative Structure-Activity Relationship (QSAR) Models
12.5.3 Challenges and Opportunities in Property Prediction
12.6 Molecular Representation and Embedding
12.6.1 Encoding Chemical Structures for Generative Models
12.6.2 Graph Neural Networks (GNNs) in Catalyst Design
12.6.3 Embedding Techniques for Catalyst Descriptor Generation
12.7 Challenges and Considerations
12.7.1 Data Quality and Bias in Training Datasets
12.7.2 Transferability and Generalization of Generative Models
12.7.3 Ethical Considerations in AI-Driven Catalyst Design
12.8 Future Directions and Emerging Technologies
12.8.1 Advanced Generative Models on the Horizon
12.8.2 Integration with High-Throughput Experimentation
12.8.3 Collaborative Approaches in Catalyst Design
Conclusion
Reference
13. Transforming Petroleum and Polymers Industry with AI
13.1 Petrochemicals as Sustainable Materials for the Modern World
13.2 Polymers
13.2.1 Polymerization Process
13.2.2 Polymer Synthesis from Petrochemicals
13.2.3 Polymer Characterization
13.2.4 Role of Catalyst in Polymerization
13.3 Advanced Polymer Materials
13.3.1 High-Performance Polymers for Specialized Applications
13.3.2 Bio-Based and Sustainable Polymers
13.3.3 Polymer Composites for Enhanced Properties
13.4 Applying Machine Learning for Polymer Research
13.4.1 Polymer Representations
13.4.2 Generating New Polymer Chemistries
13.4.3 Prediction of Properties for Sequence Defined Polymers
13.4.4 Polymer Composites for Enhanced Properties
13.4.5 Autonomous Experimentation
13.5 Membrane Design for Petroleum Research
13.5.1 Membrane Materials
13.5.2 Membrane Types and Usage
13.5.3 Revolutionizing Membrane Design Using Machine Learning
13.5.4 Topology Optimization
13.5.5 Predictive Models for Water Permeability and Salt Rejection
13.6 Interpretable Discovery of Innovative Polymers and Membranes with AI
Conclusion
References
14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery
14.1 Introduction to Natural Products Drug Discovery
14.1.1 Overview of Traditional Drug Discovery Methods
14.1.2 Importance of Natural Products in Drug Development
14.1.3 Challenges in Natural Products Drug Discovery
14.2 Data Integration and Analysis
14.2.1 Integration of Biological and Chemical Data
14.2.2 Computational Approaches for Natural Products Screening
14.2.3 Data Mining Techniques in Drug Discovery
14.3 Predictive Modeling in Natural Products Research
14.3.1 Predictive Modeling for Bioactivity
14.3.2 Structure-Activity Relationship (SAR) Analysis
14.3.3 QSAR (Quantitative Structure-Activity Relationship) Models
14.4 Target Identification and Validation
14.4.1 Identification of Drug Targets Using AI
14.4.2 Validation of Targets in Natural Products Drug Discovery
14.4.3 Challenges and Solutions in Target Identification
14.5 Database Development for Natural Products
14.5.1 Necessity for the Natural Products Databases
14.5.2 NEIMPDB and OSADHI
14.6 Prediction of Targets and Biological Activity of Natural Products
14.7 Visualizing and Navigating the Natural Products Space in Chemical Space
14.8 The Natural Product Database Landscape
Conclusion
References
15. Applying Machine Learning in Drug Repurposing
15.1 Drug Repurposing
15.1.1 Overview of Traditional Drug Development
15.1.2 Rationale for Drug Repurposing
15.2 Role of Machine Learning and Artificial Intelligence in Drug Repurposing
15.2.1 Evolution of AI in Drug Discovery
15.2.2 Machine Learning Approaches in Repurposing
15.2.3 Applications of AI in Identifying Repurposable Drugs
15.2.4 Data Integration and Analysis for Drug Repurposing
15.3 Integration of Biomedical Data Sources
15.3.1 Computational Approaches for Data Analysis
15.3.2 Utilizing Real-World Evidence (RWE) in Repurposing
15.4 Network Pharmacology and Drug Repurposing
15.4.1 Network-Based Approaches to Drug Repurposing
15.4.2 Analysis of Biological Pathways and Networks
15.4.3 Predictive Modeling in Network Pharmacology
15.5 Predictive Analytics for Drug Repurposing
15.5.1 Predictive Modeling Techniques
15.5.2 Machine Learning Algorithms for Prediction
15.5.3 Quantitative Structure-Activity Relationship (QSAR) in Repurposing
15.6 High-Throughput Screening and Virtual Screening in Drug Repurposing
15.6.1 Automation in High-Throughput Screening
15.6.2 Virtual Screening Using AI Algorithms
15.6.3 Integration of Experimental and Computational Screening
15.7 Identification of Novel Targets for Drug Repurposing
15.7.1 Target Identification Using AI
15.7.2 Validation of Targets for Repurposing
15.7.3 Challenges and Strategies in Target Identification
15.8 Combination Therapy and Synergistic Drug Repurposing
15.8.1 AI-Guided Combination Therapy Strategies
15.8.2 Identifying Synergistic Drug Combinations
15.8.3 Challenges and Opportunities in Combination Repurposing
15.9 Ethical and Regulatory Considerations in Drug Repurposing
15.9.1 Ethical Issues in AI-Driven Drug Repurposing
15.9.2 Regulatory Compliance and Safety Assessment
15.9.3 Balancing Innovation with Ethical and Legal Frameworks
15.10 Implications for the Future of Drug Repurposing with AI
Conclusion
References
16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry
16.1 Introduction
16.2 Fourth Industrial Revolution and the Rise of Industry 5.0
16.3 Role of Artificial Intelligence (AI) in Industry 5.0
16.4 Towards an AI-Assisted, Automated Chemistry Lab
16.4.1 Robotics and AI
16.4.2 Concept of the "Self-Driving" Lab
16.4.3 Remaining Hurdles to Realize the Vision of Industry 5.0
16.4.4 Will AI Ever Replace Human Chemical Intuition?
16.5 AI in Industry 5.0: Driving Smart Manufacturing
16.5.1 The Role of AI in Predictive Maintenance and Quality Control
16.5.2 AI-Powered Robotics and Automation
16.5.3 AI-Driven Supply Chain Optimization
16.5.4 AI-Driven Materials Databases and Repositories
16.6 Challenges and Opportunities of Industry 5.0
16.7 Future Trends in Reaction Modelling
16.7.1 Advances in Computational Approaches
16.7.2 Integration of Machine Learning and AI
16.7.3 Emerging Applications and Challenges
16.7.4 Importance of Reaction Modelling in Science and Industry
16.8 Machine Learning for Materials Simulation
16.8.1 High Throughput Virtual Screening
16.8.2 Analyzing Experimental Data
16.8.3 Active Learning for Materials
16.9 Various Tools for Computational Chemistry Using AI
16.10 Evolution of Chemical and Biological Space Using AI
16.11 Open-Source Tools of Computational Chemistry Using AI, ML, and DL
16.12 Latest Interventions of AI in Computational Chemistry
16.13 Future Prospects
Conclusion
References
1.1 A Comprehensive Introduction to AI
1.2 Chemical Space and AI
1.3 Impact of AI in Computational Chemistry
1.4 Machine Learning Applications in Computational Chemistry
1.4.1 ML and QSAR
1.4.2 Property Prediction
1.4.3 Generative Models for Molecular Design
1.4.4 Chemical Reactions
1.5 AI-Driven Approaches in Quantum Chemistry
1.5.1 Quantum Property Prediction
1.5.2 Quantum Circuit Optimization
1.5.3 Quantum Machine Learning for Molecular Systems
1.6 Future and Challenges of AI in Chemistry
Conclusion
References
2. Machine Learning
2.1 Fundamental Concepts of Machine Learning
2.2 Understanding the Foundations
2.2.1 Human Learning and Types of Human Learning
2.2.2 Human versus Machine Learning
2.2.3 Supervised and Unsupervised Learning
2.2.4 Neural Networks and Deep Learning
2.2.5 Generative Learning
2.3 Essential Steps in Applying Machine Learning
2.3.1 Preparation and Handling of Data
2.3.2 Feature Engineering and Feature Selection
2.3.3 Model Building and Validation
2.3.4 Evaluation
2.3.5 Applicability Domain and Deployment
2.4 Machine Learning in Various Fields of Natural Sciences
2.4.1 Material Sciences
2.4.2 Chemical Sciences
2.4.3 Life Sciences
2.4.4 Environmental Sciences
2.4.5 Agricultural Sciences
2.5 From Machine Learning to Deep Learning
2.6 Rise of Generative Models and Industry 5.0
Conclusion
References
3. Scientific Computing Using Python
3.1 Python Basics
3.1.1 Creating Python Environment
3.1.2 Installation of Packages and Libraries
3.1.3 Python Workbenches
3.2 Handling Numeric Data with NumPy
3.2.1 Basic Computing Operations
3.2.2 Arrays and Indexing
3.2.3 Vectorization
3.2.4 Functions and Methods
3.2.5 Dealing with Missing Data
3.2.6 Generating Random Numbers
3.3 Utilities of Pandas
3.3.1 Reading and Handling Data with Pandas
3.3.2 Selecting and Indexing
3.3.3 Advanced Indexing
3.3.4 Handling Text Data
3.3.5 Statistical Functions with Pandas
3.4 Visualization with Matplotlib and Seaborn
3.4.1 Plotting Basics
3.4.2 Various Types of Plots in Matplotlib
3.4.3 Overlaying and Multifigure Plots
3.4.4 3-Dimensional Plotting
3.4.5 Seaborn Plot Types
3.4.6 Categorical Plots
3.4.7 Distribution and Pair Plots
3.4.8 Correlation and Heatmaps
3.5 RdKit for Chemoinformatics
3.5.1 Molecule Representations
3.5.2 Visualizing Structures
3.5.3 Basic Operations with RdKit
3.5.4 Finding Descriptors
3.5.5 Atoms and Bonds
3.5.6 Similarity and Searching Patterns
3.6 Chemypy Package
3.6.1 Basics and Installation
3.6.2 Handling Reactions
3.6.3 Chemical Kinetics
3.6.4 Finding Properties
3.6.5 Other Utilities
Conclusion
References
4. Machine Learning with Python
4.1 Scikit-learn Library in Python
4.2 Data Representation and Generation
4.2.1 Generating Synthetic Data
4.2.2 Exploring Data Sets
4.2.3 Data Preprocessing and Preparation
4.2.4 Feature Engineering
4.3 Supervised Machine Learning
4.3.1 Training for Linear Regression
4.3.2 Multi-Linear Regression
4.3.3 Classification with Logistic Regression
4.3.4 Random Forest-Based Classification
4.4 Unsupervised Machine Learning
4.4.1 Dimensionality Reduction
4.4.2 Clustering with Partitioned Algorithms
4.4.3 Hierarchical Clustering
4.5 Evaluation Metrics
4.5.1 Confusion Matrix
4.5.2 Accuracy and Error
4.5.3 Precision and Recall
4.5.4 ROC-AUC
4.5.5 Cluster Analysis Metrics
4.6 Case Studies
Conclusion
References
5. Evolution of Computational Chemistry
5.1 Overview of Computational Chemistry
5.1.1 Computational Tools and Techniques
5.1.2 Significance and Contributions
5.2 Era of High-Performance Computing
5.2.1 Role of Supercomputing in Computational Chemistry
5.2.2 Parallelization and Acceleration Techniques
5.2.3 Cloud Computing and Distributed Computing
5.3 Software and Tools
5.3.1 Overview of Computational Chemistry Software
5.3.2 Open-Source vs. Commercial Software
5.3.3 Popular Software Packages and Their Capabilities
5.3.4 Intersection of Machine Learning and Computational Chemistry
5.3.5 Predictive Modelling and Property Estimation
5.4 Recent Advances and Future Directions
5.4.1 Quantum Computing and Its Impact
5.4.2 Multiscale Modelling and Simulation
5.4.3 Emerging Fields and Interdisciplinary Applications
Conclusion
References
6. Structure-Property Relationships
6.1 Fundamentals of Structure-Property Relationships
6.1.1 Defining Structure and Property
6.1.2 Understanding Structure-Property Relationships
6.1.3 Interlinking Molecular/Structural Features and Properties
6.2 Chemical Structure and Property Correlations
6.2.1 Molecular Structure and Properties
6.2.2 Electronic Structure and Optical Properties
6.2.3 Topological and Geometrical Descriptors
6.3 Quantitative Structure-Property Relationships (QSPR)
6.3.1 Developing QSPR Models
6.3.2 Regression Analysis and Parameterization
6.3.3 Applicability and Limitations
6.4 Quantitative Structure-Activity Relationships (QSAR)
6.4.1 QSAR in Drug Design
6.4.2 Molecular Descriptors in QSAR
6.4.3 Predictive Modelling and Toxicology
6.5 Materials Science and Structure-Property Relationships
6.5.1 Atomic and Crystal Structures
6.5.2 Mechanical Properties of Materials
6.5.3 Thermodynamic and Electronic Properties
6.6 Biological Systems and Structure-Property Relationships
6.6.1 Proteins and Enzymes
6.6.2 DNA and RNA
6.6.3 Structure-Function Relationships in Biology
Conclusion
References
7. Reaction Modelling
7.1 Overview of Reaction Modelling
7.2 Chemical Kinetics
7.2.1 Basics of Chemical Reactions
7.2.2 Reaction Rate and Rate Laws
7.2.3 Factors Affecting Reaction Rates
7.3 Reaction Mechanisms
7.3.1 Elementary Reactions vs. Overall Reactions
7.3.2 Reaction Intermediates
7.3.3 Reaction Mechanism Determination
7.3.4 Analysis of Reaction Potential Energy Surface
7.4 Reaction Rate Constants
7.4.1 Arrhenius Equation
7.4.2 Temperature Dependence
7.4.3 Catalysis and Reaction Rate Constants
7.5 Reaction Modelling Approaches
7.5.1 Homogeneous vs. Heterogeneous Reactions
7.5.2 Batch, Plug Flow, and Continuous Stirred Tank Reactors
7.5.3 Ideal vs. Non-Ideal Reactors
7.6 Numerical Methods for Reaction Modelling
7.6.1 Finite Difference Methods
7.6.2 Finite Element Methods
7.6.3 Computational Fluid Dynamics (CFD)
Conclusion
References
8. Computer-Aided Drug Design
8.1 Introduction to Computer-Aided Materials (Drug) Design
8.2 Drug Discovery Process
8.2.1 Target Identification and Validation
8.2.2 High-Throughput Screening (HTS)
8.2.3 Hit-to-Lead Optimization
8.2.4 Lead Optimization and Preclinical Testing
8.3 Molecular Modeling in Drug Design
8.3.1 Protein Structure Prediction
8.3.2 Ligand Docking and Binding Affinity Prediction
8.3.3 Pharmacophore Modeling
8.3.4 Quantitative Structure-Activity Relationship (QSAR) Studies
8.4 Virtual Screening and Compound Selection
8.4.1 Structure-Based Virtual Screening
8.4.2 Ligand-Based Virtual Screening
8.4.3 Fragment-Based Drug Design
8.5 De Novo Drug Discovery
8.5.1 De Novo Molecular Design
8.5.2 Computer-Generated Molecule Libraries
8.5.3 Optimization Algorithms in Rational Drug Design
8.6 Chemoinformatics and Bioinformatics
8.6.1 Molecular Databases and Data Mining
8.6.2 Sequence Analysis in Drug Discovery
8.6.3 Chemoinformatics for Compound Analysis
8.7 ADME/Toxicity Prediction
8.7.1 Absorption, Distribution, Metabolism, and Excretion (ADME)
8.7.2 Predicting Drug Toxicity
8.7.3 Risk Assessment in Drug Design
Conclusion
References
9. Materials Modelling
9.1 Introduction
9.1.1 Role of Materials Modelling in Science and Engineering
9.1.2 Overview of Computational Methods
9.2 Materials
9.2.1 Predicting Mechanical Properties
9.2.1.1 Strength and Elasticity
9.2.1.2 Ductility and Toughness
9.3 Material Design for Specific Applications
9.3.1 Aerospace Materials
9.3.2 Automotive Materials
9.3.3 Building and Construction Materials
9.4 Electronic and Photonic Materials
9.4.1 Semiconductor Device Simulation
9.4.1.1 Transistor Design
9.4.1.2 Optoelectronic Device Modelling
9.5 Superconductors and Magnetic Materials
9.5.1 High-Temperature Superconductors
9.5.2 Magnetic Data Storage Materials
9.6 Energy Materials
9.6.1 Fuel Cell and Battery Materials
9.6.1.1 Lithium-Ion Batteries
9.6.1.2 Fuel Cell Catalysts
9.6.2 Solar Cell Materials
9.6.2.1 Photovoltaic Device Optimization
9.6.2.2 Organic Solar Cells
9.7 Nanomaterials and Nanotechnology
9.7.1 Modeling at the Nanoscale
9.7.2 Nanoparticle Synthesis and Properties
9.7.3 Nanocomposite Materials
Conclusion
References
10. Electronic Structure Calculation, Ab Initio, DFT, and MD Simulation
10.1 Introduction to Quantum Mechanics
10.1.1 Wave Functions, Probability Densities and Operators
10.1.2 Postulates of Quantum Mechanics
10.1.3 The Time-Independent Schrödinger Equation
10.2 Molecular Hamiltonians and Operators
10.2.1 Born-Oppenheimer Approximation
10.2.2 Hamiltonian Operators
10.2.3 Expectation Values and Observables
10.3 Basis Sets and Wave Function Expansions
10.3.1 Atomic Orbitals and Basis Functions
10.3.2 Gaussian Basis Sets
10.3.3 Slater-Type Orbitals (STOs)
10.3.4 Types of Basis Sets
10.3.5 Plane Waves and Fourier Transforms
10.4 Introduction to Ab Initio Calculations
10.4.1 Hartree-Fock Theory
10.4.2 Configuration Interaction (CI)
10.4.3 Many-Body Perturbation Theory (MBPT)
10.5 Coupled Cluster Theory
10.5.1 Cluster Operators and Excitations
10.5.2 Single and Double Excitations (CCSD)
10.5.3 Higher-Order Excitations (CCSD(T))
10.6 Density Functional Theory (DFT)
10.6.1 Hohenberg−Kohn Theorem
10.6.2 Kohn-Sham Equations
10.6.3 Local Density Approximation (LDA)
10.6.4 Generalized Gradient Approximation (GGA)
10.6.5 Dispersion Corrected DFT
10.6.6 Hybrid Functional Theory
10.7 Advanced Topics in DFT
10.7.1 Time-Dependent DFT (TDDFT)
10.7.2 Linear Response and Excitation Energies
10.7.3 Optical Properties and Spectroscopy
10.7.4 Beyond TDDFT: Nonlinear Response
10.8 DFT for Strongly Correlated Systems
10.8.1 Hubbard U and DFT+U
10.8.2 Dynamical Mean Field Theory (DMFT)
10.8.3 Quantum Monte Carlo and DFT+QMC
10.9 Molecular Dynamics (MD) Simulation
10.9.1 Introduction
10.9.2 MD Using Simple Models
10.9.3 MD with Continuous Potentials
10.9.4 MD at Constant Temperatures and Pressures
10.9.5 MD with Solvent Effects: Mean Force and Stochastic Dynamics
10.9.6 Conformational Changes Post MD Simulation
10.10 Quantum Mechanics/Molecular Mechanics (QM/MM)
10.10.1 Hybrid Methods Overview
10.10.2 Implementation and Applications
10.10.3 Challenges and Considerations
10.11 Advanced MD Techniques
10.11.1 Umbrella Sampling
10.11.2 Metadynamics
10.11.3 Replica Exchange Molecular Dynamics (REMD)
10.11.4 Ab Initio Molecular Dynamics (AIMD)
10.12 Machine Learning in MD
10.12.1 Force Field Parametrization
10.12.2 Enhanced Sampling with ML
10.12.3 Other Important Methods
10.13 Simulation of Biomolecular Complexes
10.13.1 Protein-Ligand Complexes
10.13.2 Protein-Nucleic Acid Interactions
10.13.3 Protein-Protein Interactions: Large Protein Assemblies and Their Interactions
10.13.4 Membrane Proteins and Lipid Bilayers: Proteins Embedded in Lipid Membranes
Conclusion
References
11. The Chemical Space
11.1 The Concept of Chemical Space
11.2 Importance in Chemistry and Beyond
11.3 The Chemical Spaces
11.3.1 Chemical Space for Pharmacy
11.3.2 Pharmacophore Space
11.3.3 Polypharmacology
11.3.4 Chemical Space for Natural Products
11.3.5 Chemical Space for Biology and Medicine
11.4 Docking for Virtual Screening of Chemical Space
11.4.1 Different Open-Source Docking
11.4.2 ML and DL Docking Tools for Chemical Space
11.5 Chemoinformatic Resources for Chemical Space
11.5.1 Various Chemical Space Databases
11.5.2 Open-Source Platforms for Chemoinformatics
11.5.3 Online Tools Developed for Mining Chemical and Target Spaces
11.5.4 Useful Servers for Mining Chemical and Target Spaces of Target Families or Diseases
11.6 Dimensions of Chemical Space
11.6.1 Structural-Based Dimensions
11.6.2 Descriptor-Based Dimensions
11.7 Advanced Approaches to Explore the Chemical Space
11.8 AI-ML Techniques and Tools for Chemical Space
Conclusion
References
12. Generative Models for Novel Catalyst Design
12.1 Introduction
12.2 Foundations of Catalyst Design
12.2.1 Background and Significance of Catalyst Design
12.2.2 The Evolution of Catalyst Development Approaches
12.2.3 Role of Generative Models in Accelerating Catalyst Innovation
12.2.4 Traditional Catalyst Discovery Methods and Limitations
12.2.5 Need for Accelerated and Targeted Catalyst Design
12.3 Generative Models in Chemistry
12.3.1 Overview of Generative Models
12.3.2 Machine Learning and Deep Learning in Chemistry
12.3.3 Applications of Generative Models in Catalyst Design
12.4 Types of Generative Models
12.4.1 Variational Autoencoders (VAEs)
12.4.2 Generative Adversarial Networks (GANs)
12.4.3 Reinforcement Learning in Catalyst Design
12.4.4 Comparative Analysis of Generative Model Types
12.5 Catalyst Property Prediction
12.5.1 Predictive Modeling for Catalyst Activity and Selectivity
12.5.2 Quantitative Structure-Activity Relationship (QSAR) Models
12.5.3 Challenges and Opportunities in Property Prediction
12.6 Molecular Representation and Embedding
12.6.1 Encoding Chemical Structures for Generative Models
12.6.2 Graph Neural Networks (GNNs) in Catalyst Design
12.6.3 Embedding Techniques for Catalyst Descriptor Generation
12.7 Challenges and Considerations
12.7.1 Data Quality and Bias in Training Datasets
12.7.2 Transferability and Generalization of Generative Models
12.7.3 Ethical Considerations in AI-Driven Catalyst Design
12.8 Future Directions and Emerging Technologies
12.8.1 Advanced Generative Models on the Horizon
12.8.2 Integration with High-Throughput Experimentation
12.8.3 Collaborative Approaches in Catalyst Design
Conclusion
Reference
13. Transforming Petroleum and Polymers Industry with AI
13.1 Petrochemicals as Sustainable Materials for the Modern World
13.2 Polymers
13.2.1 Polymerization Process
13.2.2 Polymer Synthesis from Petrochemicals
13.2.3 Polymer Characterization
13.2.4 Role of Catalyst in Polymerization
13.3 Advanced Polymer Materials
13.3.1 High-Performance Polymers for Specialized Applications
13.3.2 Bio-Based and Sustainable Polymers
13.3.3 Polymer Composites for Enhanced Properties
13.4 Applying Machine Learning for Polymer Research
13.4.1 Polymer Representations
13.4.2 Generating New Polymer Chemistries
13.4.3 Prediction of Properties for Sequence Defined Polymers
13.4.4 Polymer Composites for Enhanced Properties
13.4.5 Autonomous Experimentation
13.5 Membrane Design for Petroleum Research
13.5.1 Membrane Materials
13.5.2 Membrane Types and Usage
13.5.3 Revolutionizing Membrane Design Using Machine Learning
13.5.4 Topology Optimization
13.5.5 Predictive Models for Water Permeability and Salt Rejection
13.6 Interpretable Discovery of Innovative Polymers and Membranes with AI
Conclusion
References
14. Application of Machine Learning and Artificial Intelligence in Natural Products Drug Discovery
14.1 Introduction to Natural Products Drug Discovery
14.1.1 Overview of Traditional Drug Discovery Methods
14.1.2 Importance of Natural Products in Drug Development
14.1.3 Challenges in Natural Products Drug Discovery
14.2 Data Integration and Analysis
14.2.1 Integration of Biological and Chemical Data
14.2.2 Computational Approaches for Natural Products Screening
14.2.3 Data Mining Techniques in Drug Discovery
14.3 Predictive Modeling in Natural Products Research
14.3.1 Predictive Modeling for Bioactivity
14.3.2 Structure-Activity Relationship (SAR) Analysis
14.3.3 QSAR (Quantitative Structure-Activity Relationship) Models
14.4 Target Identification and Validation
14.4.1 Identification of Drug Targets Using AI
14.4.2 Validation of Targets in Natural Products Drug Discovery
14.4.3 Challenges and Solutions in Target Identification
14.5 Database Development for Natural Products
14.5.1 Necessity for the Natural Products Databases
14.5.2 NEIMPDB and OSADHI
14.6 Prediction of Targets and Biological Activity of Natural Products
14.7 Visualizing and Navigating the Natural Products Space in Chemical Space
14.8 The Natural Product Database Landscape
Conclusion
References
15. Applying Machine Learning in Drug Repurposing
15.1 Drug Repurposing
15.1.1 Overview of Traditional Drug Development
15.1.2 Rationale for Drug Repurposing
15.2 Role of Machine Learning and Artificial Intelligence in Drug Repurposing
15.2.1 Evolution of AI in Drug Discovery
15.2.2 Machine Learning Approaches in Repurposing
15.2.3 Applications of AI in Identifying Repurposable Drugs
15.2.4 Data Integration and Analysis for Drug Repurposing
15.3 Integration of Biomedical Data Sources
15.3.1 Computational Approaches for Data Analysis
15.3.2 Utilizing Real-World Evidence (RWE) in Repurposing
15.4 Network Pharmacology and Drug Repurposing
15.4.1 Network-Based Approaches to Drug Repurposing
15.4.2 Analysis of Biological Pathways and Networks
15.4.3 Predictive Modeling in Network Pharmacology
15.5 Predictive Analytics for Drug Repurposing
15.5.1 Predictive Modeling Techniques
15.5.2 Machine Learning Algorithms for Prediction
15.5.3 Quantitative Structure-Activity Relationship (QSAR) in Repurposing
15.6 High-Throughput Screening and Virtual Screening in Drug Repurposing
15.6.1 Automation in High-Throughput Screening
15.6.2 Virtual Screening Using AI Algorithms
15.6.3 Integration of Experimental and Computational Screening
15.7 Identification of Novel Targets for Drug Repurposing
15.7.1 Target Identification Using AI
15.7.2 Validation of Targets for Repurposing
15.7.3 Challenges and Strategies in Target Identification
15.8 Combination Therapy and Synergistic Drug Repurposing
15.8.1 AI-Guided Combination Therapy Strategies
15.8.2 Identifying Synergistic Drug Combinations
15.8.3 Challenges and Opportunities in Combination Repurposing
15.9 Ethical and Regulatory Considerations in Drug Repurposing
15.9.1 Ethical Issues in AI-Driven Drug Repurposing
15.9.2 Regulatory Compliance and Safety Assessment
15.9.3 Balancing Innovation with Ethical and Legal Frameworks
15.10 Implications for the Future of Drug Repurposing with AI
Conclusion
References
16. From Industry 4.0 to Industry 5.0: The Role of AI and Computational Chemistry
16.1 Introduction
16.2 Fourth Industrial Revolution and the Rise of Industry 5.0
16.3 Role of Artificial Intelligence (AI) in Industry 5.0
16.4 Towards an AI-Assisted, Automated Chemistry Lab
16.4.1 Robotics and AI
16.4.2 Concept of the "Self-Driving" Lab
16.4.3 Remaining Hurdles to Realize the Vision of Industry 5.0
16.4.4 Will AI Ever Replace Human Chemical Intuition?
16.5 AI in Industry 5.0: Driving Smart Manufacturing
16.5.1 The Role of AI in Predictive Maintenance and Quality Control
16.5.2 AI-Powered Robotics and Automation
16.5.3 AI-Driven Supply Chain Optimization
16.5.4 AI-Driven Materials Databases and Repositories
16.6 Challenges and Opportunities of Industry 5.0
16.7 Future Trends in Reaction Modelling
16.7.1 Advances in Computational Approaches
16.7.2 Integration of Machine Learning and AI
16.7.3 Emerging Applications and Challenges
16.7.4 Importance of Reaction Modelling in Science and Industry
16.8 Machine Learning for Materials Simulation
16.8.1 High Throughput Virtual Screening
16.8.2 Analyzing Experimental Data
16.8.3 Active Learning for Materials
16.9 Various Tools for Computational Chemistry Using AI
16.10 Evolution of Chemical and Biological Space Using AI
16.11 Open-Source Tools of Computational Chemistry Using AI, ML, and DL
16.12 Latest Interventions of AI in Computational Chemistry
16.13 Future Prospects
Conclusion
References
- Edition: 1
- Latest edition
- Published: August 1, 2026
- Language: English
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