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...