Metamorphosis of Computational Chemistry Driven by Artificial Intelligence and Industry 5.0

CHAPTER 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 CHAPTER 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 Step 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 CHAPTER 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-Dimenstional Plotting 3.4.5 Seaborn Plot Types 3.4.6 Categorical Plots with 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 Atom 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 CHAPTER 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 CHAPTER 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 CHAPTER 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 CHAPTER 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 Reference CHAPTER 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 CHAPTER 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 CHAPTER 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 CHAPTER 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 11.5.3Online 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 CHAPTER 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 CHAPTER 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 CHAPTER 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 14.4.1 Validation of Targets in Natural Products Drug Discovery 14.4.1 Challenges and Solutions in Target Identification 14. 5 Database development for Natural products 14.5.1 Necessity for the Natural Products Databases 14.5.2NEIMPDB 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 CHAPTER 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 15.6.3Integration 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 CHAPTER 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 Analysing 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