Artificial Intelligence and Machine Learning in Drug Design and Development E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Fintech in a Sustainable Digital Society
• Sprache: Englisch

Seitenzahl: 1373

Veröffentlichungsjahr: 2024

Beschreibung

The book is a comprehensive guide that explores the use of artificial intelligence and machine learning in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs.

The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine.

AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine.

This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being.

The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers 19 state-of-the-art chapters providing valuable insights and a compass to navigate the exciting journey ahead.

Audience

The book is a valuable resource for a wide range of professionals in the pharmaceutical and allied industries including researchers, scientists, engineers, and laboratory workers in the field of drug discovery and development, who want to learn about the latest techniques in machine learning and AI, as well as information technology professionals who are interested in the application of machine learning and artificial intelligence in drug development.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 The Rise of Intelligent Machines: An Introduction to Artificial Intelligence

1.1 Introduction

1.2 Key Components of Artificial Intelligence

1.3 Applications of Artificial Intelligence

1.4 Generative AI

1.5 Ethical and Societal Implications of AI

1.6 Ethical AI Development

1.7 Future of AI

1.8 Conclusion

References

2 Introduction to Bioinformatics

List of Abbreviations

2.1 Introduction

2.2 Key Concepts in Bioinformatics

2.3 Bioinformatics Tools and Databases

2.4 Applications in Bioinformatics

2.5 Challenges and Opportunities in Bioinformatics

2.6 Future Directions of Bioinformatics in Drug Design

2.7 Conclusion and Future Scope

References

3 Exploring the Intersection of Biology and Computing: Road Ahead to Bioinformatics

3.1 Introduction

3.2 Bioinformatics in Systems Biology

3.3 Tools and Techniques in Bioinformatics

3.4 Bioinformatics in Precision Medicine

3.5 Challenges in Bioinformatics

3.6 Research Directions

3.7 Conclusion and Future Scope

Future Scope and Potential Opportunities

References

4 Machine Learning in Drug Discovery: Methods, Applications, and Challenges

4.1 Introduction

4.2 Applications of AI and ML in Drug Discovery

4.3 AI and ML Methods to Drug Discovery

4.4 Challenges

4.5 Conclusion and Future Directions

References

5 Artificial Intelligence for Understanding Mechanisms of Antimicrobial Resistance and Antimicrobial Discovery: A New Age Model for Translational Research

5.1 Introduction

5.2 Commonly Used Artificial Intelligence Algorithms for AMR

5.3 AI for Understanding Mechanisms of AMR and Antimicrobial Discovery

5.4 Strategies to Overcome Antibiotic Resistance

5.5 Applications of Artificial Intelligence for Antimicrobial Resistance

5.6 Challenges Towards Practical Implementation

5.7 Conclusion and Future Scope

References

6 Artificial Intelligence-Powered Molecular Docking: A Promising Tool for Rational Drug Design

6.1 Introduction

6.2 Basics of Molecular Docking

6.3 Role of Artificial Intelligence in Molecular Docking

6.4 Drug Discovery in the New Age

6.5 Drug Discovery Using Machine Learning (ML) Algorithms

6.6 Drug Discovery Using Deep Learning (DL) Algorithms

6.7 AI-Based Toolkits Used for Drug Discovery

6.8 Applications of AI in Molecular Docking

6.9 Challenges and Limitations of AI-Based Molecular Docking

6.10 Conclusion and Future Scope

Future Directions

References

7 Revolutionizing Drug Discovery: The Roleof AI and Machine Learning in Accelerating Medicinal Advancements

7.1 Introduction

7.2 Machine Learning Techniques in Drug Discovery

7.3 AI Techniques for Prediction and Analysis of Drugs

7.4 AI for Revolutionizing Drug Development

7.5 Challenges and Solutions

7.6 Conclusion and Future Scope

Future Scope

References

8 Data Processing Method for AI-Driven Predictive Models for CNS Drug Discovery

8.1 Introduction

8.2 The Role of AI And ML in Drug Discovery

8.3 Role of AI and ML in Central Nervous System (CNS)

8.4 The Effect of AI/ML on CNS Drug Research

8.5 Prospects for AI/ML in CNS Drug Research in Future

8.6 Proposed Methodology on Data Processing for CNS Drug-Likeness Prediction

8.7 Conclusion and Future Scope

References

9 Machine Learning Applications for Drug Repurposing

List of Abbreviations

9.1 Introduction

9.2 Trends in ML Applications for Drug Repurposing

9.3 Understanding Drug Repurposing

9.4 Traditional Techniques in Drug Repurposing

9.5 Modern Technologies in Drug Repurposing

9.6 Data Sources for Drug Repurposing

9.7 Case Studies: Applications of Machine Learning in Drug Repurposing

9.8 Future of Machine Learning in Drug Repurposing

9.9 Conclusion and Future Scope

References

10 Personalized Drug Treatment: Transforming Healthcare with AI

10.1 Introduction

10.2 Cheminformatics

10.3 Data Sources

10.4 Precision Medicine vs. Personalized Drug Treatment

10.5 AI Models for Healthcare

10.6 Ethical Considerations in AI-Enabled Personalized Drug Treatment

10.7 Benefits and Limitations of AI-Enabled Personalized Drug Treatment

10.8 Case Studies

10.9 Conclusion, Challenges, and Opportunities

References

11 Process and Applications of Structure-Based Drug Design

11.1 Introduction

11.2 Structure-Based Drug Design: Steps

11.3 Tools and Techniques Used in Structure-Based Drug Design

11.4 Applications

11.5 Other Examples

11.6 Advantages and Limitations of a Structure-Based Drug Design

11.7 Case Studies and Examples

11.8 Future Outlook and Implications

11.9 Potential Impact on Healthcare and Drug Development

11.10 Conclusion and Future Scope

References

12 AI-Based Personalized Drug Treatment

12.1 Introduction

12.2 How AI Can Improve Drug Treatment?

12.3 Techniques Used in AI-Based Drug Treatment

12.4 Case Studies and Examples

12.5 Challenges and Limitations of AI-Based Drug Treatment

12.6 Future Outlook and Implications

12.7 Conclusion and Future Work

References

13 AI Models for Biopharmaceutical Property Prediction

List of Abbreviations

13.1 Introduction

13.2 AI Models for Biopharmaceutical Property Prediction

13.3 Recent Advances in AI Models for Biopharmaceutical Property Prediction

13.4 Case Study: COVID-19 Vaccines

13.5 Current Research in Applications of AI for Biopharmaceuticals

13.6 Future Directions and Challenges

13.7 Conclusion and Future Scope

References

14 Deep Learning Tactics for Neuroimaging Genomics Investigations in Alzheimer’s Disease

14.1 Introduction

14.2 Pathophysiology of Alzheimer’s Disease

14.3 Deep Learning Tactics in the Prediction, Classification, and Diagnosis of AD

14.4 Deep Learning-Based Identification of Genetic Variants

14.5 Deep Learning-Based Prediction of Altered Genes and mRNA Levels in AD

14.6 Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

14.7 Limitations and Challenges in Deep Learning-Based Neuroimaging Genomics Investigations in Alzheimer’s Disease

14.8 Future Prospects for Applying Deep Learning Techniques in Alzheimer’s Disease Treatment Environments

14.9 Conclusion and Future Scope

References

15 Artificial Intelligence Techniques in the Classification and Screening of Compounds in Computer-Aided Drug Design (CADD) Process

15.1 Introduction

15.2 Overview of the Drug Design Process

15.3 Computational Tools and Techniques in CADD

15.4 Concept of Artificial Intelligence (AI) and Machine Learning (ML) Methods

15.5 Major Machine Learning (ML) Techniques and Applications in Molecular Screening Process

15.6 Challenges and Opportunities

15.7 Conclusion and Future Perspectives

References

16 Empowering Clinical Decision Making: An In-Depth Systematic Review of AI-Driven Scoring Approaches for Liver Transplantation Prediction

16.1 Introduction

16.2 Review Methodology

16.3 A Comprehensive Literature Review of AI-Driven Scoring Methods for Predicting Liver Transplantation Outcomes

16.4 Discussion and Insights

16.5 Conclusion and Future Scope

References

17 Pushing Boundaries: The Landscape of AI-Driven Drug Discovery and Development with Insights Into Regulatory Aspects

17.1 Introduction

17.2 Classification of AI

17.3 Overview of AI Technologies Used in DDS

17.4 Applications of AI in DDS and Drug DVPT

17.5 Ethical Considerations Regarding the Use of AI in DDS and DVPT

17.6 IPR Issues

17.7 Regulatory Approval and Market Access

17.8 AI in Medicine Current DVPTs and Strategy for Pharmaceutical Companies

17.9 Conclusion and Future Perspectives

References

18 Feasibility of AI and Robotics in Indian Healthcare: A Narrative Analysis

18.1 Introduction

18.2 Robotics and Their Types in Healthcare

18.3 Pros of Robotics in Healthcare

18.4 Insights Into Robotic Surgeries in India

18.5 Limitations of Robotics in Healthcare

18.6 Future Applications of Robotics and AI

18.7 Conclusion and Future Scope

Future Scope

References

19 The Future of Healthcare: AIoMT—Redefining Healthcare with Advanced Artificial Intelligence and Machine Learning Techniques

19.1 Introduction

19.2 Application of AI and ML in Drug Design and Development

19.3 Secure AIoMT Framework for Smart Healthcare

19.4 AIoMT Cybersecurity Aspects

19.5 AIoMT Threats, Attacks, and Countermeasures

19.6 Selected Case Studies

19.7 Conclusion and Future Scope

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Glossary of key bioinformatics terms.

Table 2.2 Comparative analysis of pairwise and multiple sequence alignment met...

Table 2.3 Comparison of approaches for gene and protein structure prediction....

Table 2.4 Comparison of

de novo

and reference-guided genomic assembly methods....

Table 2.5 Overview of major biological databases.

Table 2.6 Comparison of bioinformatics software tools.

Table 2.7 Common bioinformatics algorithms and their applications.

Table 2.8 Comparison of different cloud-based platforms for bioinformatics.

Table 2.9 Summary of omics data and their applications in drug discovery.

Table 2.10 Summary of the different bioinformatics and drug design approaches ...

Table 2.11 Case studies of bioinformatics in drug discovery and development.

Table 2.12 Future directions of bioinformatics in drug design and development....

Chapter 4

Table 4.1 Databases for drug discovery.

Chapter 5

Table 5.1 AI models employed for predicting AMR genes.

Table 5.2 AI models employed for the discovery of antimicrobial peptides.

Table 5.3 ML models employed for combating antifungal resistance.

Table 5.4 ML models employed for combating antiviral resistance.

Table 5.5 ML models employed for combating antiparasitic resistance.

Table 5.6 ML models employed for combating biofilms.

Chapter 6

Table 6.1 List of some common docking tools along with their algorithms.

Table 6.2 Machine learning toolkits used widely for drug design and developmen...

Chapter 9

Table 9.1 Examples of repurposing drugs approved for other indications based o...

Table 9.2 Examples of repurposed drugs approved for other indications based on...

Table 9.3 Comparison of machine learning techniques for drug repurposing.

Table 9.4 Deep learning techniques for drug repurposing.

Table 9.5 Software tools for machine learning in drug repurposing.

Table 9.6 Summary of datasets used in machine learning for drug repurposing.

Table 9.7 Summary of clinical datasets used in machine learning for drug repur...

Chapter 10

Table 10.1 Details of software.

Chapter 13

Table 13.1 Comparison of the three commercial AI models used for biopharmaceut...

Chapter 14

Table 14.1 Deep learning tactics for predicting Alzheimer’s disease risk using...

Chapter 15

Table 15.1 Major computational tools for drug designing (CADD) and application...

Table 15.2 Small molecule database.

Table 15.3 Some of the major tools involved in molecular property generation a...

Chapter 16

Table 16.1 Review of the state-of-the-art AI-based scoring methods for liver t...

Table 16.2 Parameters of CTP score and its values.

Table 16.3 Various parametric values of MILAN and UCSF criteria

Table 16.4 Various parametric values of Up to Seven score.

Table 16.5 A comprehensive review of AI and clinical scoring method integratio...

Table 16.6 Various parameters included in clinical scoring methods.

Chapter 17

Table 17.1 Types of ML [12].

Table 17.2 USFDA-approved AI-based applications.

Table 17.3 Panel-wise USFDA-approved AI-based applications as medical devices ...

Chapter 19

Table 19.1 Summary of AI algorithms and their applications in AI-enabled healt...

List of Illustrations

Chapter 1

Figure 1.1 Artificial intelligence and its allied domains.

Figure 1.2 Key components of artificial intelligence.

Figure 1.3 Types of machine learning algorithms.

Figure 1.4 Deep learning and its types.

Figure 1.5 Applications of artificial intelligence in the computer vision doma...

Figure 1.6 Common applications of natural language processing.

Figure 1.7 Generative AI-enabling technologies.

Figure 1.8 Major applications of generative AI.

Figure 1.9 Industries using generative AI to expand their reach, providing inn...

Chapter 2

Figure 2.1 The interdisciplinary nature of bioinformatics.

Figure 2.2 Workflow of a typical bioinformatics analysis.

Figure 2.3 Illustration of the interaction between the binding pocket of 3K1E ...

Figure 2.4 Diagram illustrating various applications of bioinformatics.

Chapter 3

Figure 3.1 Workflow of bioinformatics problem solving process.

Figure 3.2 System flow diagram of biological systems in bioinformatics.

Figure 3.3 An interdisciplinary field that develops methods for understanding ...

Figure 3.4 Purposed genome bioinformatics competencies.

Chapter 4

Figure 4.1 Machine learning paradigms.

Figure 4.2 Linear classifiers for binary classification (two classes).

Figure 4.3 Schematic diagram of logistic regression.

Figure 4.4 Schematic diagram of k-nearest neighbors.

Figure 4.5 Schematic diagram of decision tree.

Figure 4.6 Schematic diagram of random forest.

Figure 4.7 Schematic diagram of ANN [26].

Figure 4.8 Challenges of AI and ML in drug discovery.

Chapter 5

Figure 5.1 Schematic workflow of an AI-powered omics-based analysis and proces...

Figure 5.2 Challenges involved in the practical implementation of AI.

Chapter 6

Figure 6.1 Illustration of molecular docking.

Figure 6.2 Key steps involved in docking.

Figure 6.3 Most commonly used scoring functions.

Figure 6.4 Types of docking.

Figure 6.5 Widely used ML algorithms.

Figure 6.6 Deep learning algorithms.

Figure 6.7 Deepchem workflow chart.

Chapter 7

Figure 7.1 Drug discovery using deep learning: a word cloud.

Figure 7.2 Characteristics of NLP-based language processing.

Figure 7.3 Algorithms in GA-based feature selection approach: SVM, ANN, KNN, a...

Figure 7.4 Various drug discovery clustering approaches.

Figure 7.5 RF functioning.

Figure 7.6 SVM working.

Figure 7.7 Working of Bayesian model.

Chapter 8

Figure 8.1 The use of AI and ML in the pharmaceutical R & D process.

Figure 8.2 The stages involved in developing an AI model.

Figure 8.3 Target identification using AI. In order to expedite the developmen...

Figure 8.4 CNS disease treatment has benefited from advancements made possible...

Figure 8.5 Proposed methodology on data processing for CNS drug-likeness predi...

Chapter 9

Figure 9.1 Publication trends of machine learning for drug repurposing from Ja...

Figure 9.2 Distribution of publications on machine learning for drug repurposi...

Figure 9.3 Clustering of research themes identified through keyword co-occurre...

Figure 9.4 Research trends in the application of machine learning for drug rep...

Figure 9.5 Comparative timeline illustrating the speed and efficiency of

de no

...

Figure 9.6 Chemical structure of sildenafil (ChemDraw 20.1.1).

Figure 9.7 Chemical structures of galantamine (left) and clemastine (right) (C...

Figure 9.8 Chemical structures of minoxidil, finasteride, levocetirizine, and ...

Chapter 10

Figure 10.1 Types of chemical structure representation.

Figure 10.2 Scopus analysis for document type distribution.

Figure 10.3 Publication landscape: comparative analysis of countries/regions....

Figure 10.4 Temporal evolution of publications: Scopus analysis.

Chapter 11

Figure 11.1 Structure-based drug discovery process.

Figure 11.2 Timeline of the historical milestones of drug design.

Figure 11.3 Three-dimensional structure of myoglobin.

Figure 11.4 Structure of hemoglobin.

Figure 11.5 Safety measures for therapeutic compounds.

Figure 11.6 Scaffold hopping.

Figure 11.7 Workflow of SBDD.

Figure 11.8 NMR spectroscopy.

Figure 11.9 Homology modeling.

Figure 11.10 Molecular docking.

Figure 11.11 Molecular dynamics simulations.

Figure 11.12 Types of CVDs.

Figure 11.13 HIV protease inhibitors.

Figure 11.14 (a) Anilino quinazoline derivative. (b) Pyridopyrimidine derivati...

Figure 11.15 Influenza neuraminidase inhibitors.

Figure 11.16 Chemical structure depiction of imatinib.

Figure 11.17 Chemical structure depiction of raltegravir.

Figure 11.18 Mechanism of action of venetoclax.

Figure 11.19 Chemical structure of osimertinib.

Chapter 12

Figure 12.1 Background process on personalized medicine.

Figure 12.2 Overview of AI in drug discovery.

Figure 12.3 Pharmaceutical areas with an AI footprint.

Figure 12.4 Prediction of drug efficacy.

Figure 12.5 Identifying patient sub-groups.

Figure 12.6 Process of personalized medicine using AI.

Figure 12.7 Usage of ML in healthcare.

Figure 12.8 AI models used in AI-based drug development.

Figure 12.9 IBM Watson for drug discovery.

Figure 12.10 Benevolent AI’s drug discovery platform.

Figure 12.11 Challenges of AI-based drug treatment.

Figure 12.12 Ethical considerations.

Chapter 13

Figure 13.1 Categories of biopharmaceuticals.

Figure 13.2 The correlations and differences between artificial intelligence, ...

Figure 13.3 Flowchart illustrating the typical steps involved in using artific...

Figure 13.4 Types of machine learning (ML) models for biopharmaceutical proper...

Figure 13.5 Linear regression in machine learning for predicting biopharmaceut...

Figure 13.6 The architecture of a multilayer perceptron-based artificial neura...

Figure 13.7 The architecture of the convolutional neural network for machine l...

Figure 13.8 The architecture of recurrent neural networks for machine learning...

Figure 13.9 Structural components and functional aspects of G protein-coupled ...

Figure 13.10 Trends in publications based on research articles between 1 Janua...

Chapter 14

Figure 14.1 Hallmarks of Alzheimer’s disease.

Figure 14.2 The fusion framework of a three-level AI network for the predictio...

Figure 14.3 An example of a transformer model used in bioinformatics, together...

Figure 14.4 Phenotypic classification process using deep learning.

Figure 14.5 Use of linear discriminant analysis to locate the genes in the 2D ...

Figure 14.6 Integrating neuroimaging and genomics to predict AD risk. CNNs, co...

Chapter 15

Figure 15.1 Overview of a computer-aided drug design process.

Figure 15.2 Process of molecular docking.

Figure 15.3 Methods to perform molecular dynamics simulation.

Figure 15.4 Steps followed in the QSAR modeling process.

Figure 15.5 Major milestones of artificial intelligence development.

Figure 15.6 Steps followed in implementing AI in the screening of compounds.

Figure 15.7 Support vector machine algorithms in the classification of compoun...

Figure 15.8 (a) Functioning of biological neurons. (b) Basic architecture of A...

Chapter 16

Figure 16.1 Efficient clinical decision-making in liver transplantation.

Figure 16.2 PRISMA flow diagram of systematic identification, screening, eligi...

Figure 16.3 AI-driven workflow for liver transplantation prediction.

Figure 16.4 Accuracy assessment of AI-based scoring methods for liver transpla...

Figure 16.5 The 3-month mortality rate with various ranges of MELD score.

Figure 16.6 Two-year survival rate with CTP score.

Figure 16.7 (a) Comprehensive overview of donor factors included in the Donor ...

Figure 16.8 Accuracy shown by various clinical scoring methods in different AI...

Chapter 17

Figure 17.1 Potential applications of AI in healthcare.

Figure 17.2 Classification of artificial intelligence.

Figure 17.3 Ten guiding principles for good ML practices as developed by USFDA...

Chapter 18

Figure 18.1 Surgical robot used in Max super Specialty Hospital, New Delhi, In...

Figure 18.2 Computer-assisted surgery used in Apollo Hospital, New Delhi, Indi...

Figure 18.3 Rehabilitation robotics process in Kokilaben Hospital, Mumbai, Ind...

Figure 18.4 Socially assistive robotics made by NAO robots.

Figure 18.5 List of government hospitals that used robotic technology in India...

Figure 18.6 Timeline of robotic surgery in India.

Figure 18.7 Procedures involved in robotic surgeries.

Chapter 19

Figure 19.1 1950–2022 artificial intelligence evolution in the healthcare indu...

Figure 19.2 Artificial intelligence and machine learning applications in healt...

Figure 19.3 Secure artificial intelligence of the Internet of Medical Things e...

Figure 19.4 Real-time artificial intelligence of the Internet of Medical Thing...

Guide

Cover Page

Series Page

Title Page

Copyright Page

Preface

Table of Contents

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Artificial Intelligence and Machine Learning in Drug Design and Development

Edited by

Abhirup Khanna

School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

May El Barachi

Faculty of Engineering & Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, United Arab Emirates

Sapna Jain

Department of Applied Sciences and Humanities (Chemistry), University of Petroleum and Energy Studies, Dehradun, India

Manoj Kumar

Faculty of Engineering & Information Sciences, University of Wollongong in Dubai, Dubai Knowledge Park, United Arab Emirates

and

Anand Nayyar

School of Computer Science, Duy Tan University, Da Nang, Viet Nam

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Preface

The intersection of Artificial Intelligence (AI) and Machine Learning (ML) within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine.

AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. In traditional methods of drug design, searching for a drug that exhibits desired biological activities while conforming to safe pharmacological profiles can be a long, costly, and challenging task. Complex methods are employed to identify new chemical compounds that may be developed and eventually marketed as drugs. Despite all the technological progress, the process is very long, with an estimated average of 9 to 12 years, and the success rate is low, which considerably increases the total cost.

This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals. This book is a testament to the profound impact these technologies have had and will continue to have on the pharmaceutical industry, healthcare, and ultimately, patient well-being.

The editors of this volume have assembled a distinguished group of experts, researchers, and thought leaders from both the AI, ML, and pharmaceutical domains. Their collective knowledge and insights illuminate the multifaceted landscape of AI and ML in drug design and development, offering a roadmap for navigating its complexities and harnessing its potential. In each section, readers will find a rich tapestry of knowledge, case studies, and expert opinions, providing a 360-degree view of AI and ML’s role in drug design and development. Whether you are a researcher, scientist, industry professional, policymaker, or simply curious about the future of medicine, this book offers valuable insights and a compass to navigate the exciting journey ahead.

The book comprises 19 chapters providing an overview of the state-of-the-art in the development and application of AI, ML, and DL methods in drug design and development. Chapter 1, “The Rise of Intelligent Machines: An Introduction to Artificial Intelligence,” gives a foundational approach towards Artificial Intelligence and Generative AI, and comprehensively covers various ethical and societal implications of AI development. Chapter 2, “Introduction to Bioinformatics,” provides a comprehensive overview of bioinformatics in terms of principles, methodologies, applications, and emerging trends while also highlighting how it serves as an interdisciplinary bridge between biology and computer science. In addition, the chapter specifies the significance of bioinformatics in various biological research domains and other application areas using real-time scenarios.

Chapter 3, “Exploring the Intersection of Biology and Computing: Road Ahead to Bioinformatics,” discusses the importance of bioinformatics and also its relation to drug discovery and development. In addition, the chapter discusses the need for powerful computational resources in the field of bioinformatics, as well as data privacy and heterogeneity. Chapter 4, “Machine Learning in Drug Discovery: Methods, Applications, and Challenges,” highlights the uses of Machine Learning algorithms in different phases of drug discovery and development (such as target validation); discusses the challenges and limitations inherent to ML techniques in drug discovery; and showcases various existing works on drug discovery that use ML tools and techniques and other current advancements for drug development.

Chapter 5 explores the use of AI to perform analysis on various data sources—e.g., Genomics, Proteomics, and metabolomics data—and specifies how AI-driven algorithms are employed to find associations and trends in large, complex datasets about AMR. The chapter also explains how to apply AI algorithms to optimize the design of antimicrobial compounds, facilitating the translation of AI-driven findings into clinical practice and public health policies. Chapter 6, “Artificial Intelligence Powered Molecular Docking: A Promising Tool for Rational Drug Design” presents various AI techniques in drug discovery, and highlights molecular docking along with its applications. The chapter also discusses various challenges encountered in implementing AI in docking algorithms and proposes potential solutions.

Chapter 7, “Revolutionizing Drug Discovery: The Role of AI and Machine Learning in Accelerating Medicinal Advancements,” highlights the potential of AI, ML, DL, NLP, and robotics in drug design and development. Furthermore, the chapter presents a detailed analysis of ML algorithms and explores the diverse facets of AI in domains like personalized medicine, drug reallocation, safety assessments, predictive analysis, and drug formulation. Chapter 8, “Data Processing Method for AI-Driven Predictive Models for CNS Drug Discovery,” presents ideas on how AI can be used to generate drugs, and highlights AI and ML advancements in CNS drug design, along with various advanced applications like drug repurposing, drug synergy prediction, de nova drug design, and drug sensitivity prediction. In addition, the chapter illustrates various pharmaceutical research directions for AI and ML in drug discovery.

Chapter 9, “Machine Learning Applications for Drug Repurposing,” explores ML techniques used in drug repurposing and the challenges faced by ML in drug repurposing. It also gives research directions for the application of ML techniques in drug repurposing. Chapter 10, “Personalized Drug Treatment: Transforming Healthcare with AI,” looks at the fundamentals of AI in healthcare; explores data sources and collection methods for personalized treatment; and illustrates various case studies specifying AI’s impact on personalized drug treatment. In addition, the chapter discusses regulator and ethical considerations in AI-enabled personalized medicine.

Chapter 11, “Process and Applications of Structure-Based Drug Design,” examines the various steps involved in structure-based drug design, and the tools and techniques used in structure-based drug design, applications. The chapter outlines the advantages and limitations of structure-based drug design, and discusses some future implications and potential impacts. Chapter 12, “AI Based Drug Development,” details how AI improves drug development and the techniques required; enlists challenges and limitations of AI-based drug development; and highlights some case studies and examples to illustrate AI’s importance in drug development. Chapter 13, “AI Models for Biopharmaceutical Property Prediction,” describes the principles, advantages, and challenges of AI models used for biopharmaceutical property prediction; discusses ML and AL advancements in drug design and development; and enumerates the limitations and future challenges associated with the implementation of AI models for biopharmaceutical property prediction.

Chapter 14, “Deep Learning Tactics for Neuroimaging Genomics Investigations in Alzheimer’s Disease,” discusses deep learning tactics in the prediction, classification, and diagnosis of Alzheimer’s disease, and explains deep learning-based prediction of altered genes and mRNA in Alzheimer’s disease. Chapter 15, “Artificial Intelligence Techniques in the Classification and Screening of Compounds in Computer Aided Drug Design (CADD) Process,” reviews the computational tools and techniques in CADD, elaborates on AI and ML methods in the molecular screening process, and illustrates the associated challenges and opportunities.

Chapter 16, “Empowering Clinical Decision Making: An In-Depth Systematic Review of AI-Driven Scoring Approaches for Liver Transplantation Problem,” explores various AI-based scoring methods employed in liver transplantation to enhance clinical decision-making efficiency, and assesses the accuracy and predictive performance of these AI-based scoring methods in predicting post-transplant outcomes, encompassing graft failure, rejection, and patient survival. Furthermore, the chapter examines the impact of AI-based scoring methods on clinical decision-making efficiency pertaining to liver transplantation, while focusing on resource allocation, waiting times, workflow optimization, and overall transplant program outcomes. The chapter also analyzes the characteristics that affect how well AI-based scoring techniques are implemented and integrated into routine clinical decision-making in regards to liver transplantation.

Chapter 17, “Pushing Boundaries: The Landscape of AI-driven Drug Discovery and Development with Insights into Regulatory Aspects,” highlights AI technologies used in drug design and discovery; chronicles the applications of AI in DDS and Drug DVPT; and elaborates on AI in medicine, current DVPTs, and a strategy for pharmaceutical companies. Chapter 18, “Feasibility of AI and Robotics in Indian Healthcare: A Narrative Analysis,” describes various types of robotics in healthcare and thoroughly discusses the inclusion of robotics in Indian hospitals, using real-time case studies. The chapter also considers future applications of robotics and AI.

Chapter 19, “The Future of Healthcare: AIoMT- Redefining Healthcare with Advanced Artificial Intelligence and Machine Learning Techniques,” explores many technologies used in drug design and development, and proposes a novel and secure AIoMT framework for smart healthcare. Additionally, the chapter discusses various case studies that demonstrate early detection of diabetic retinopathy, chatbots employed for mental health, and predictive analytics for patients’ outcomes.

We are deeply grateful to everyone who helped with this book and greatly appreciate the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during its publication.

Abhirup KhannaMay El BarachiSapna JainManoj KumarAnand Nayyar

March 2023

1The Rise of Intelligent Machines: An Introduction to Artificial Intelligence

Shamik Tiwari

School of Computer Science, UPES, Dehradun, India

Abstract

Artificial intelligence (AI) represents a field within computer science dedicated to developing intelligent machines that can execute tasks typically demanding human intelligence. AI aims to create algorithms, systems, and tools that replicate cognitive processes, including language comprehension, problem-solving, learning, and reasoning. AI is a multidisciplinary field that draws inspiration from various areas, including computer science, mathematics, neuroscience, philosophy, psychology, and linguistics. The emergence of AI has resulted in a revolutionary period in human history. Industry, society, and our perception of computer capabilities are all being influenced by the growth of intelligent machines, which are being powered by AI technology. The main concepts, purposes, latest developments, and ethical concerns of AI and intelligent machines are summarized in this chapter.

Keywords: Artificial intelligence, machine learning, neural networks, deep learning, intelligent machines, AI applications

1.1 Introduction

Artificial intelligence is the term used to describe computer systems that simulate human cognitive processes. It includes the capacity of computers to carry out operations such as problem-solving, learning, reasoning, perception, language understanding, and decision-making that ordinarily call for human intelligence. Artificial intelligence (AI) technologies attempt to build systems that duplicate and enhance human cognitive abilities, changing how we communicate with technology and our environment. The development of devices that could imitate human thought processes marked the beginning of AI. Key milestones include Alan Turing’s theoretical framework for computation, the Dartmouth Workshop in 1956 that coined the term “artificial intelligence,” and the development of early AI programs like the Logic Theorist and the General Problem Solver [1, 2].

The founding father of AI, Alan Turing, defines this discipline as:

“AI is the science and engineering of making intelligent machines, brilliant computer programs.”

Artificial intelligence can also be defined as follows:

The potential of a robot or other device operated by a program to carry out tasks usually performed by intelligent beings.

A computational system with artificial intelligence displays behavior that is typically regarded to require intelligence.

It is the replication by machines, particularly computer systems, of how human intellect works. These procedures entail self-correction, inference, and learning.

A machine’s capacity to mimic intelligent human behavior.

The critical question is “How close or how well a computer can imitate or go beyond when compared with a human being,” even though the above definitions are all appropriate. Figure 1.1 provides the sub-domains of artificial intelligence.

AI can be broadly categorized into two main types [3]:

Narrow AI (weak AI): Narrow AI refers to AI systems designed and trained for specific tasks and operating within a limited domain. Examples of narrow AI applications include virtual assistants like Siri or Alexa, recommendation systems on online platforms, and image recognition algorithms.

Figure 1.1 Artificial intelligence and its allied domains.

General AI (strong AI): General AI aims to replicate human-like intelligence and abilities across various tasks. It refers to AI systems that can understand, learn, and reason about various domains, just as humans do. General AI is still largely theoretical and remains a significant challenge in the field.

Organization of the Chapter

The rest of the chapter is organized as: A detailed examination of AI key components are enlightened in Section 1.2. Notable applications are explored in Section 1.3, followed by an in-depth discussion of generative AI in Section 1.4 and ethical and societal implications of AI in Section 1.5. Sections 1.6 and 1.7 focus on ethical AI development and the future of AI. And, finally, Section 1.8 concludes the chapter with future scope.

1.2 Key Components of Artificial Intelligence

Artificial intelligence is a multidisciplinary field that includes several essential elements and methods. These elements combine to allow machines to mimic human intellect and carry out activities that call for thinking, problem-solving, perception, and learning. Figure 1.2 provides the key components of artificial intelligence.

1.2.1 Machine Learning (ML)

Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computer systems to learn and improve their performance on a specific task by analyzing and adapting to data without being explicitly programmed. Figure 1.3 details various machine learning algorithms [4, 5].

Figure 1.2 Key components of artificial intelligence.

a. Supervised learning: In supervised learning, artificial intelligence models are developed on labeled data, where the algorithm learns to classify or predict objects based on input–output relationships. The algorithm learns to predict outcomes in labeled data by examining trends and connections between input and output variables. Examples of applications for supervised learning include fraud detection, recommendation systems, and image and speech recognition. Classification and regression are the two primary supervised learning techniques. While classification classifies data into preset groups or labels, regression forecasts continuous numeric values.

A continuous numerical output or value is predicted using regression, a supervised learning problem. The regression algorithm discovers a correlation between the input features and a continuous target variable. Regression models produce numerical values as their output, and their typical objective is to reduce the discrepancy between the projected values and the actual values in the training data. Regression algorithms commonly used include regression trees, polynomials, and linear regression. Regression tasks include assessing a patient’s blood pressure based on health indicators, projecting stock prices, and predicting housing prices based on factors like square footage and location.

Classification, on the other hand, is a supervised learning activity that seeks to sort incoming data into predetermined groups or classifications. When performing classification, the algorithm learns to associate input features with specific labels or classes. A class label identifying the input’s category serves as the output of a classification model. Examples of standard categorization techniques include logistic regression, decision trees, support vector machines, and neural networks. The classification of emails as spam or not, the identification of objects in photographs, and the classification of patients into different illness categories are a few examples of classification jobs.

Figure 1.3 Types of machine learning algorithms.

b. Unsupervised learning: Unsupervised learning, frequently used for clustering or dimensionality reduction, entails training AI models using unlabeled data to find underlying patterns or structures within the data. Clustering, dimensionality reduction, anomaly detection, association rule learning, and generative models are just a few of the crucial methods included in unsupervised learning. Clustering algorithms like K-means group data points with similar properties, whereas dimensionality reduction techniques like PCA identify key aspects from highly dimensional data. Outliers or odd data points are found using anomaly detection, linkages in transactional data are found using association rule learning, and fresh data samples are created using generative models like GANs and VAEs. These unsupervised learning approaches play a vital role in data exploration, pattern discovery, and various applications where labeled data is scarce or unavailable, enabling valuable insights and data representations.

c. Semi-supervised learning: Semi-supervised learning uses a larger pool of unlabeled data for model training and a smaller amount of labeled data. This approach is especially helpful when obtaining labeled data is difficult or impractical. It uses the knowledge in the unlabeled data to enhance model performance using supervised and unsupervised learning techniques. Two common approaches are pseudo-labeling, in which predictions are formed on unlabeled data and utilized as pseudo-labels, and adapting conventional supervised algorithms to the semi-supervised environment. Semi-supervised learning improves model performance and makes better use of the resources available in various fields where labeled data is rare.

d. Reinforcement learning: Reinforcement learning (RL) involves an agent interacting with its surroundings and performing behaviors to maximize cumulative rewards over time. RL does not rely on labeled data like supervised learning; it learns by making mistakes instead. The agent experiments with several options and considers the effects of its choices before altering its plan to complete its objectives successfully. Applications for RL can be found in various domains, including improving recommendation systems, playing strategic games, and training autonomous robots. It addresses sequential decision-making issues, making it an essential strategy for developing intelligent, adaptable systems that can learn from experience and enhance performance in changing circumstances [6].

1.2.2 Deep Learning (DL)

Deep learning, a branch of machine learning, focuses on using multiple-layered artificial neural networks (deep neural networks) to model and complete challenging tasks. These deep neural networks are made to recognize and learn hierarchical patterns and characteristics from data automatically. Deep learning has been quite effective in applications like speech recognition, natural language processing, and image recognition. It is called “deep” because it employs numerous layers of connected artificial neurons, allowing it to handle complex and high-dimensional data representations [7] successfully.

A computer model can perform categorization tasks directly from images, text, or sound by applying deep learning techniques. Contemporary deep learning models can achieve remarkable precision, occasionally surpassing human performance. Training these models involves the utilization of a substantial dataset with labeled examples and deploying multi-layered neural network architectures. One widely used subtype of deep neural networks is Convolutional Neural Network (CNN or ConvNet). CNNs incorporate 2D convolutional layers and integrate learned features with input data, making them exceptionally well suited for processing 2D data, such as images. Knowing the features utilized to classify images is optional because CNNs conduct the manual feature extraction for you. Direct feature extraction from photos is how CNN operates. The pertinent features are not trained; they are discovered as the network is trained on a set of images. Thanks to this automated feature extraction, deep learning models are incredibly accurate for computer vision applications like object categorization. Figure 1.4 presents types of various deep learning approaches.

Figure 1.4 Deep learning and its types.

1.2.3 Expert System (ES)

An expert system, sometimes called a knowledge-based system, is a computer program or software application that simulates a human expert’s judgment and problem-solving abilities in a particular subject area. Expert systems use a knowledge base of facts, laws, and heuristics that capture expertise in a particular subject area to provide intelligent advice, make recommendations, or solve issues [8]. DENDRAL, a tool for predicting molecule structure during chemical analysis, is one example of an expert system. PXDES is another illustration of an expert system that foretells the nature and severity of lung cancer.

1.2.4 Natural Language Processing (NLP)

With artificial intelligence, machines can now comprehend and interpret human language in addition to reading. It entails the development of computational models and algorithms to enable computers to comprehend, interpret, and produce human language in a meaningful and useful way. To enable computers to interact with and manage human language as if they were fluent in it, NLP comprises a wide range of tasks, including text analysis, language translation, sentiment analysis, speech recognition, and more [9].

Computational linguistics, a rule-based approach to modeling human language, is combined with other models like statistical models, machine learning, and deep learning in NLP. When combining various technological models, computers can process spoken or written words to represent human language. They can understand the complete meaning as a result, which includes the speaker’s or writer’s intentions and emotions. Search engine functioning is an illustration of NLP in action. Using user intent and past search history, search engines employ NLP to propose appropriate results [10].

1.2.5 Computer Vision (CV)

A subset of artificial intelligence called computer vision enables machines to interpret and understand visual information from images and videos. It encompasses diverse tasks, including image preprocessing, relevant feature extraction, object detection, image classification, and even semantic segmentation, enabling a finer understanding of image details. Computer vision finds applications across various domains, including autonomous driving, facial recognition, medical imaging, quality control in manufacturing, augmented reality, and surveillance systems [11]. This technology has fundamentally transformed our perception and interaction with the visual world surrounding us. Face recognition is one such example of computer vision. Computer vision is used in facial recognition technologies to locate specific individuals in images and movies. Law enforcement agencies can use it to track suspicious persons. However, in its most basic form, it is utilized by businesses like Meta or Google to recommend people to tag in images [12].

1.2.6 Machine Perception

Machine perception denotes the capacity of AI systems to perceive and understand their environment through sensors such as cameras, microphones, and accelerometers. By employing techniques like sensor fusion and deep learning, machines can effectively engage with their surroundings by amalgamating data from multiple modalities, identifying objects, and discerning emotions [13]. This expanded capability opens potential applications across various domains, including autonomous vehicles, healthcare diagnostics, immersive virtual experiences, and human–computer interaction. It enhances machines’ ability to comprehend and respond to intricate sensory information.

1.2.7 Intelligent Agents (IAs)

Intelligent or AI agents are software entities created to interact with their surroundings and make decisions to accomplish specified goals. From straightforward rule-based systems to sophisticated decision-makers, AI agents cover many AI applications [14]. Intelligent agents, in contrast, place a more significant priority on autonomy and flexibility and are frequently used in dynamic, real-time situations, such as robots and autonomous systems. Both are essential to AI because they provide different levels of reasoning and decision-making power to handle various problems and tasks.

1.3 Applications of Artificial Intelligence

Artificial intelligence has various applications across various industries and domains, transforming our work and lives. Some notable applications of AI in various sectors are provided below [15–30].

I. Computer vision: AI has numerous applications in computer vision, where it enhances the capabilities of machines to interpret and understand visual data from the world. Figure 1.5 presents applications of artificial intelligence in the computer vision domain. The following are some critical AI applications in computer vision.

Image classification: Identifying objects or patterns within images.

Object detection: Detecting and locating specific objects within images or videos.

Facial recognition: Recognizing and verifying individuals based on facial features.

Video analytics: Analyzing video content for various purposes, such as surveillance and content recommendation.

Optical character recognition (OCR): AI-powered OCR systems can recognize and convert printed or handwritten text into machine-readable text.

Gesture recognition: AI can recognize and interpret hand gestures, enabling touchless interfaces and control in applications like gaming, virtual reality, and human–machine interaction.

Figure 1.5 Applications of artificial intelligence in the computer vision domain.

Augmented Reality (AR) and Virtual Reality (VR): AI in machine vision tracks and analyzes the user’s environment, enhancing AR and VR experiences.

Sports analytics: AI-enhanced cameras and vision systems analyze sports events, providing real-time data for coaching.

II. Natural language processing: AI has a wide range of applications in NLP, which is a subfield of AI focused on the interaction between computers and human language. Figure 1.6 shows common applications of natural language processing. Following are the few key applications of AI in NLP domain.

Text classification: Categorizing text documents into predefined classes or categories.

Sentiment analysis: The process of determining text’s sentiment or emotional tone, which is commonly employed in social media monitoring and customer feedback analysis.

Machine translation: NLP is used in machine translation systems such as Google Translate to translate text from one language to another automatically.

Speech recognition: Speech recognition systems utilize NLP algorithms to translate spoken words into written text. This is used in voice assistants such as Siri and Google Assistant and transcription services.

Question answering: NLP models can be used to create question-answering systems that can respond to user questions using a corpus of text or a knowledge base.

Figure 1.6 Common applications of natural language processing.

Text summarizing: Text summarizing algorithms may automatically generate succinct summaries of long text documents, making enormous amounts of information easier to comprehend.

Language generation: NLP models like GPT-3, 4 can generate human-like text, which can be used for content generation, chatbots, and creative writing assistance.

Chatbots and virtual assistants: Natural language processing is essential for developing chatbots and virtual assistants to engage in natural discussions with users, answer inquiries, and execute tasks.

Information retrieval: NLP is used in search engines to increase the search result accuracy by comprehending the user’s query and web page content.

Text-to-voice (TTS) conversion: NLP technology turns written text into natural-sounding voice in TTS systems. Voiceovers, accessibility tools, and navigation systems all use this.

III. Recommendation systems: Systems that recommend goods, services, or content to customers based on their tastes and behavior are greatly aided by artificial intelligence (AI). Systems for making recommendations using AI have many uses in various sectors of the economy. The primary AI applications in recommendation systems are listed below.

Collaborative filtering: Recommending products, services, or content based on user behavior and preferences.

Content-based filtering: Recommending items based on their features and similarity to items users have shown interest in.

IV. Healthcare: AI can be used in various ways in the healthcare industry to improve administrative, diagnostic, and patient care procedures significantly. Below are a few significant uses of AI in healthcare.

Medical image analysis: Assisting in interpreting medical images, such as X-rays, MRIs, and CT scans.

Disease diagnosis: Aiding in the early detection and diagnosis of diseases based on patient data.

Drug discovery: Accelerating discovering new drugs and identifying potential drug candidates.

Drug interaction and adverse event detection: AI systems identify potential drug interactions and adverse events by analyzing patient data and medication histories.

Virtual health assistants: AI-powered chatbots and virtual assistants provide patients with medical information, answer questions, and schedule appointments.

V. Finance: In finance, AI has many uses that are revolutionizing the sector in many ways. Following are a few significant financial AI applications.

Credit scoring: Assessing the creditworthiness of individuals or businesses.

Algorithmic trading: Making financial trading decisions based on historical and real-time data.

Fraud detection: Identifying fraudulent transactions or activities in banking and financial systems.

VI. Autonomous Vehicles: The creation and use of autonomous vehicles (AVs) heavily relies on artificial intelligence. AVs rely on AI technologies to perceive their surroundings, make real-time decisions, and navigate safely. Following are a few significant AI uses for autonomous vehicles:

Self-driving cars: Enabling vehicles to navigate and make decisions autonomously using sensor data and machine learning algorithms.

V2X communication: AI plays a part in vehicle-to-everything (V2X) communication, which enables autonomous vehicles to communicate with other vehicles, infrastructure, and traffic control systems.

VII. Industry and manufacturing: AI applications in industries such as manufacturing are accelerating improvements in output, sustainability, and quality, resulting in more competitive and effective manufacturing processes. Below are some important AI uses in manufacturing and industry.

Predictive maintenance: Predicting equipment failures and optimizing maintenance schedules to reduce downtime.

Quality control: Identifying defects and ensuring product quality in real time on production lines.

VIII. Retail: The retail sector is changing thanks to AI, which is strengthening decision-making, enhancing operations, and improving customer experiences. The critical uses of AI in retail include the following:

Demand forecasting: Predicting consumer demand for products to optimize inventory management.

Personalized marketing: Delivering tailored marketing and product recommendations to individual customers.

IX. Marketing and advertising: By offering data-driven insights, automating tasks, and enabling more individualized and targeted campaigns, AI is having a significant impact on the marketing and advertising sector. Some prominent uses of AI in marketing and advertising are given as follows:

Customer segmentation: Identifying target audiences.

Ad click prediction: Maximizing ad campaign effectiveness.

Price optimization.

X. Agriculture: AI applications in agriculture promote more resilient, efficient, and sustainable farming methods. It enables farmers to meet global food demand while reducing their environmental effects. The following are some significant uses of AI in agriculture:

Crop monitoring: Using remote sensing and sensor data for crop health assessment and yield prediction.

Precision agriculture: Optimizing farming practices for efficiency and resource conservation.

Weed and pest control: AI-based robotic systems can autonomously identify and eliminate weeds or pests in fields.

Weather prediction: Models based on AI use weather data analysis to produce precise forecasts and climate predictions, which assist farmers in making plans for shifting weather patterns and modifying their farming practices accordingly.

XI. Energy management: AI is becoming increasingly important in energy management by optimizing resource allocation, increasing efficiency, and improving sustainability. The following are some of the most essential AI applications in energy management:

Energy consumption forecasting: Predicting energy usage to optimize energy distribution and reduce costs.

Smart grids: Improving the efficiency and reliability of electricity grids.

XII. Space exploration: AI advances space exploration by improving mission capabilities, data analysis, and decision-making processes. Below are some of the most important AI uses in space exploration:

Identifying celestial objects and anomalies in space data.

Autonomous rovers.

Exoplanet discovery.

Astronaut assistance.

Mission planning and optimization.

XIII. Gaming and entertainment: AI is transforming the gaming and entertainment industries by improving gameplay, personalizing content, and overall user experiences. Following are some of the most essential AI applications in gaming and entertainment:

Character behavior and strategy development in video games.

Create realistic character animations, making movements.

To enhance graphics, rendering, and physics simulations for more immersive worlds.

AI contributes to tracking, gesture recognition, and object recognition in VR and AR experiences, making them more interactive and immersive.

XIV. Human resources: AI is increasingly used in human resources to expedite operations and improve the overall HR experience for employees and companies. Below are some significant AI uses in human resources:

Resume screening and candidate selection.

Employee churn prediction.