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Artificial Intelligence and Machine Learning in Drug Design and Development

Edited by Abhirup Khanna, May El Barachi, Sapna Jain, Manoj Kumar, and Anand Nayyar
Copyright: 2024   |   Status: Published
ISBN: 9781394234165  |  Hardcover  |  
668 pages
Price: $$245 USD
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One Line Description
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.

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.

Description
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.

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Author / Editor Details
Abhirup Khanna is an accomplished professional currently working as an assistant professor at the University of Petroleum and Energy Studies, Dehradun, India. He is an alumnus of The University of Melbourne, Australia. He has authored two books and numerous research publications in the areas of AI, blockchain technology, Internet of Things, and Cloud Computing for international journals and conferences. His research profile demonstrates his commitment to pushing the boundaries of AI and blockchain technology and his potential to drive transformative changes in these fields.

May El Barachi, PhD, is the Director of Computer Science & IT Programs at the University of Wollongong in Dubai, UAE. An Egyptian-Canadian computer scientist, and smart technology expert with degrees in telecom, engineering, computer engineering, and computer science, Dr. El Barachi holds leadership roles in teaching/learning and research. In her current role, she defines the research strategy for the faculty and ensures that the right ecosystem is established for conducting high-impact research.

Sapna Jain, PhD, is an assistant professor at the University of Petroleum and Energy Studies, Dehradun, India. She has earned her PhD in ‘Synthesis of novel bioactive compounds’ from Delhi University. She has published various research papers in renowned national and international journals, as well as two patents concerning the application of a synergistic combination of synthetic and natural products as an antifungal agent.

Manoj Kumar, PhD, is an associate professor at the University of Wollongong in Dubai, UAE as well as the Research Head for Network and Cyber Security Cluster at the university. He obtained his PhD from The Northcap University, Haryana, India. Dr. Kumar has more than 14 years of research, teaching, and corporate experience, and has published more than 175 research articles in international refereed journals and conferences.

Anand Nayyar, PhD, obtained his doctorate from Desh Bhagat University, Punjab, India in 2017 and is currently an assistant professor at the School of Computer Science, Duy Tan University, Viet Nam. He is also the Vice-Chairman of Research and Director of the IoT and Intelligent Systems Lab at Duy Tan University. He has published more than 180 research articles in international refereed journals, 50 books, and has 100+ patents to his credit. He has more than 12,000 citations on Google Scholar.

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Table of Contents
Preface
1. The Rise of Intelligent Machines: An Introduction to Artificial Intelligence
Shamik Tiwari
1.1 Introduction
1.2 Key Components of Artificial Intelligence
1.2.1 Machine Learning (ML)
1.2.2 Deep Learning (DL)
1.2.3 Expert System (ES)
1.2.4 Natural Language Processing (NLP)
1.2.5 Computer Vision (CV)
1.2.6 Machine Perception
1.2.7 Intelligent Agents (IAs)
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
Bancha Yingngam
List of Abbreviations
2.1 Introduction
2.2 Key Concepts in Bioinformatics
2.2.1 Sequence Alignment
2.2.2 Gene and Protein Structure Prediction
2.2.3 Computational Evolutionary Biology
2.2.4 Genome Assembly
2.2.5 Biological Network Analysis
2.2.6 Analysis of Gene and Protein Expression and Regulation
2.3 Bioinformatics Tools and Databases
2.3.1 Publicly Available Databases
2.3.1.1 GenBank
2.3.1.2 RCSB Protein Data Bank (RCSB PDB)
2.3.1.3 UniProt
2.3.1.4 Ensembl
2.3.1.5 KEGG
2.3.1.6 PubMed
2.3.2 Software and Tools for Data Analysis
2.3.2.1 BLAST (Basic Local Alignment Search Tool)
2.3.2.2 CLUSTAL Omega and CLUSTALW
2.3.2.3 BioPython, BioPerl, Bioconductor, and BioJava
2.3.2.4 GATK (Genome Analysis Toolkit)
2.3.2.5 PyMOL
2.3.2.6 Cytoscape
2.3.2.7 MOE (Molecular Operating Environment)
2.3.3 Cloud-Based Platform for Bioinformatics
2.3.3.1 Google Genomics
2.3.3.2 Amazon Web Services (AWS) for Genomics
2.3.3.3 Microsoft Azure for Research
2.3.3.4 IBM Watson for Genomics
2.3.3.5 Galaxy
2.3.3.6 Seven Bridges Genomics
2.4 Applications in Bioinformatics
2.4.1 Sequence Mapping of Biomolecules (DNA, RNA, and Proteins)
2.4.2 Primer Design
2.4.3 Prediction of Functional Gene Products
2.4.4 Trace Evolutionary Trees of Genes
2.4.5 Prediction of Protein Structure
2.4.6 Molecular Modeling of Biomolecules
2.4.7 Development of Models for the Functioning of Cells, Tissues, and Organs
2.4.8 Drug Design and Development
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
Ahmed Mateen Buttar, Muhammad Nouman Arshad and Anand Nayyar
3.1 Introduction
3.1.1 Medical Data
3.1.2 Sequence Alignment and Searching
3.1.3 Genomics and Functional Genomics
3.1.4 Proteomics and Protein Structure Prediction
3.1.5 Metabolomics
3.2 Bioinformatics in Systems Biology
3.2.1 Introduction to Systems Biology
3.2.2 Data Integration in Systems Biology
3.2.3 Network Analysis in Systems Biology
3.3 Tools and Techniques in Bioinformatics
3.3.1 Commonly Used Bioinformatics Software
3.3.2 Machine Learning in Bioinformatics
3.3.3 Cloud Computing in Bioinformatics
3.4 Bioinformatics in Precision Medicine
3.4.1 Definition and Importance of Precision Medicine
3.4.2 Role of Bioinformatics in Precision Medicine
3.4.3 Pharmacogenomics and Personalized Medicine
3.4.4 Case Study
3.5 Challenges in Bioinformatics
3.5.1 Data Volume and Complexity
3.5.2 Data Integration and Standardization
3.5.3 Reproducibility of Bioinformatics Analysis
3.5.4 Privacy and Security Concerns
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
Geetha Mani and Gokulakrishnan Jayakumar
4.1 Introduction
4.2 Applications of AI and ML in Drug Discovery
4.2.1 Target Validation
4.2.2 Drug Toxicity Prediction
4.2.3 Drug–Target Interaction Prediction
4.2.4 Drug Bioactivity Prediction
4.3 AI and ML Methods to Drug Discovery
4.3.1 Support Vector Machines
4.3.2 Logistic Regression
4.3.3 Naive Bayes
4.3.4 k-Nearest Neighbors
4.3.5 Decision Trees and Random Forest
4.3.6 Ensemble Learning
4.3.7 Artificial Neural Networks
4.4 Challenges
4.4.1 Data Quality and Quantity
4.4.2 Integration and Interoperability
4.4.3 Lack of Domain Expertise
4.4.4 Validation and Regulation Approval
4.4.5 Ethical Considerations
4.4.6 Cost and Infrastructure Requirements
4.4.7 Limited Generalization Capability
4.4.8 Collaboration and Data Sharing
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
Yashaswi Dutta Gupta and Suman Bhandary
5.1 Introduction
5.2 Commonly Used Artificial Intelligence Algorithms for AMR
5.3 AI for Understanding Mechanisms of AMR and Antimicrobial Discovery
5.3.1 Understanding the Mechanisms of AMR
5.3.2 AI for Antimicrobial Discovery
5.3.2.1 Targeting and Validation of Potential Targets
5.3.2.2 Drug Repurposing
5.3.2.3 Novel Antimicrobial Peptide (AMP) Discovery
5.3.2.4 Optimizing Drug Formulations
5.4 Strategies to Overcome Antibiotic Resistance
5.4.1 Discovering Novel Antibiotic Compounds
5.4.2 Antibiotic Repurposing and Optimization
5.4.3 Optimization of Combinatorial Therapy
5.4.4 Strengthening the Antibiotic Stewardship Program
5.5 Applications of Artificial Intelligence for Antimicrobial Resistance
5.5.1 AI in Combating Antifungal Resistance
5.5.2 AI in Combating Antiviral Resistance
5.5.3 AI in Combating Antiparasitic Resistance
5.5.4 AI in AMR Surveillance
5.5.5 Rapid Diagnostics
5.5.6 Combating Biofilm Through AI
5.6 Challenges Towards Practical Implementation
5.6.1 Challenges from a Microbial Perspective
5.6.2 Challenges from AI Perspective
5.6.3 Approval by Regulatory Bodies
5.7 Conclusion and Future Scope
References
6. Artificial Intelligence-Powered Molecular Docking: A Promising Tool for Rational Drug Design
Nabajit Kumar Borah, Yukti Tripathi, Aastha Tanwar, Deeksha Tiwari, Aditi Sinha, Shailja Sharma, Neetu Jabalia, Ruchi Jakhmola Mani, Seneha Santoshi and Hina Bansal
6.1 Introduction
6.2 Basics of Molecular Docking
6.2.1 Fundamental Process of Molecular Docking
6.2.2 Essential Components of Molecular Docking
6.2.3 Types of Molecular Docking
6.2.3.1 Rigid Docking
6.2.3.2 Flexible Docking
6.2.3.3 Semi-Flexible Docking
6.2.4 Common Tools and Software for Molecular Docking
6.3 Role of Artificial Intelligence in Molecular Docking
6.3.1 ML-Based Scoring Functions
6.3.2 Pose Prediction or Protein–Ligand Interactions
6.3.3 High-Throughput Virtual Screening
6.4 Drug Discovery in the New Age
6.5 Drug Discovery Using Machine Learning (ML) Algorithms
6.5.1 Random Forest (RF)
6.5.2 Naive Bayesian (NB)
6.5.3 Support Vector Machine (SVM)
6.6 Drug Discovery Using Deep Learning (DL) Algorithms
6.6.1 Multilayer Perceptrons (MLPs)
6.6.2 Recurrent Neural Networks (RNN)
6.6.3 Convolutional Neural Networks (CNN)
6.6.4 Generative Adversarial Networks (GANs)
6.7 AI-Based Toolkits Used for Drug Discovery
6.8 Applications of AI in Molecular Docking
6.8.1 Prediction of Drug–Target Interactions
6.8.2 Drug Repurposing
6.9 Challenges and Limitations of AI-Based Molecular Docking
6.10 Conclusion and Future Scope
Future Directions
References
7. Revolutionizing Drug Discovery: The Role of AI and Machine Learning in Accelerating Medicinal Advancements
Anu Sayal, Janhvi Jha, Chaithra N., Atharv Rajesh Gangodkar and Shaziya Banu S.
7.1 Introduction
7.2 Machine Learning Techniques in Drug Discovery
7.2.1 Deep Learning in the Pharmaceutical Industry
7.2.2 Natural Language Processing (NLP) Methodologies for Drug Development
7.2.3 Automated Management and Dispensing of Prescriptions Using Robotics
7.2.4 Feature Selection and Genetic Algorithm in Drug Discovery
7.2.5 Clustering Algorithms for Drug Discovery
7.3 AI Techniques for Prediction and Analysis of Drugs
7.3.1 Random Forest (RF)
7.3.2 Support Vector Machines (SVM)
7.3.3 Bayesian Network
7.3.4 ANN and CNN
7.3.5 Development of Individualized Treatment Plans
7.4 AI for Revolutionizing Drug Development
7.4.1 Personalized Medicine, Drug Repurposing, and Drug Safety Evaluation
7.4.1.1 Predictive Analytics and Precision Medicine
7.4.1.2 Virtual Screening
7.4.1.3 De Novo Design
7.5 Challenges and Solutions
7.6 Conclusion and Future Scope
Future Scope
References
8. Data Processing Method for AI-Driven Predictive Models or CNS Drug Discovery
Ajantha Devi Vairamani, Sudipta Adhikary and Kaushik Banerjee
8.1 Introduction
8.1.1 Background Information
8.2 The Role of AI And ML in Drug Discovery
8.2.1 Exposition of Machine Learning and Artificial Intelligence Algorithms
8.2.2 Molecular Fingerprints and Identifiers for Pre-Processing Data
8.2.3 Use of Machine Learning and AI for Target Recognition
8.2.4 Applications of AI in the Early Phases of Drug Target Discovery
8.2.4.1 Discovery of a Target
8.2.4.2 Deconvolution of the Target
8.2.5 AI and ML in Drug Screening
8.2.5.1 Digital Ligand-Based Screening
8.2.6 Prediction of QSAR
8.2.6.1 Artificial Intelligence and Machine Learning for Physicochemical Property and ADME/T Prediction Physicochemical Property Forecasting
8.2.7 Prediction of ADME-T
8.2.8 Use of ANNs and MLs in Drug Discovery
8.2.9 Predicting Medication Interactions Using Artificial Intelligence and Machine Learning
8.2.10 Role of AI and ML in Repurposing Existing Drugs
8.3 Role of AI and ML in Central Nervous System (CNS)
8.3.1 Hypothesis on BBB Permeability
8.3.2 Benefits of Using AI and ML in Neurological Disease Drug Discovery
8.3.2.1 Drug Discovery Using AI/ML for Neurological Illnesses
8.3.2.2 The Use of ML and AI in the Search for Antidepressants
8.3.2.3 The Use of AI and ML in Parkinson’s Disease Medication Discovery
8.3.2.4 Applications of AI and ML in Alzheimer’s Disease Medication Discovery
8.3.2.5 Medical Uses of AI and ML for Painkillers and Anesthetics
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
Bancha Yingngam
List of Abbreviations
9.1 Introduction
9.2 Trends in ML Applications for Drug Repurposing
9.2.1 Global Research on ML Applications in Drug Repurposing
9.2.2 Cluster Analysis of ML Applications in Drug Repurposing
9.2.3 Research Trends in ML Applications for Drug Repurposing
9.3 Understanding Drug Repurposing
9.4 Traditional Techniques in Drug Repurposing
9.4.1 Serendipity
9.4.2 In Vitro Screening
9.4.3 Phenotypic Screening
9.4.4 Side Effect Analysis
9.4.5 Biochemical Assay
9.4.6 Off-Label Use
9.5 Modern Technologies in Drug Repurposing
9.5.1 Machine Learning
9.5.2 Machine Learning Methods in Drug Repurposing
9.5.2.1 Supervised Learning
9.5.2.2 Unsupervised Learning
9.5.2.3 Semi-Supervised Learning
9.5.2.4 Reinforcement Learning
9.5.3 Deep Learning
9.5.4 Software Tools for Machine Learning in Drug Repurposing
9.6 Data Sources for Drug Repurposing
9.6.1 Biomedical Databases
9.6.1.1 Genomic Databases
9.6.1.2 Proteomic Databases
9.6.1.3 Chemoinformatics Databases
9.6.2 Clinical Data Sources
9.6.2.1 Electronic Health Records (EHR)
9.6.2.2 Clinical Trial Databases
9.6.2.3 Adverse Event Databases
9.7 Case Studies: Applications of Machine Learning in Drug Repurposing
9.7.1 Cancer Treatment
9.7.2 Antiviral Treatment
9.7.3 Antidiabetic Treatment
9.7.4 Neurodegenerative Disease Treatment
9.7.5 Hair Loss Treatment
9.8 Future of Machine Learning in Drug Repurposing
9.8.1 Advanced Machine Learning Techniques
9.8.2 Interdisciplinary Approaches
9.8.3 Improved Data Quality and Availability
9.8.4 Integration with Clinical Trials and Healthcare Systems
9.9 Conclusion and Future Scope
References
10. Personalized Drug Treatment: Transforming Healthcare with AI
Abhirup Khanna and Sapna Jain
10.1 Introduction
10.2 Cheminformatics
10.2.1 Chemical Data and Databases
10.2.2 Chemical Structure Representation
10.2.3 Chemical Property Prediction
10.2.4 Chemical Reaction Prediction
10.2.5 Drug Discovery and Design
10.2.6 Materials Science
10.3 Data Sources
10.4 Precision Medicine vs. Personalized Drug Treatment
10.4.1 Survey Results
10.4.2 Importance of Personalized Drug Treatment
10.5 AI Models for Healthcare
10.5.1 Personalized Drug Treatment Using AI for Cancer
10.5.2 Personalized Drug Treatment Using AI for Cardiovascular Diseases
10.5.3 Personalized Drug Treatment Using AI for Mental Health
10.6 Ethical Considerations in AI-Enabled Personalized Drug Treatment
10.7 Benefits and Limitations of AI-Enabled Personalized Drug Treatment
10.7.1 Benefits
10.7.2 Limitations
10.8 Case Studies
10.8.1 Project CancerLinQ
10.8.2 Project Atomwise
10.8.3 Project BenevolentAI
10.8.4 Project Deep Genomics
10.9 Conclusion, Challenges, and Opportunities
References
11. Process and Applications of Structure-Based Drug Design
Shanmuga Sundari M., Sree Aiswarya Thotakura, Mounika Dharmana, Priyanka Gadela and Mayukha Mandya Ammangatambu
11.1 Introduction
11.1.1 Overview of the Drug Discovery Process
11.1.2 Importance of Structure-Based Drug Design
11.1.3 Historical Background and Milestones
11.2 Structure-Based Drug Design: Steps
11.2.1 Target Identification and Validation
11.2.2 Structure Determination of Target Protein
11.2.3 Virtual Screening of Compounds
11.2.4 Hit Selection and Optimization
11.2.5 Lead Optimization and Development
11.3 Tools and Techniques Used in Structure-Based Drug Design
11.3.1 X-Ray Crystallography
11.3.2 NMR Spectroscopy
11.3.3 Homology Modeling
11.3.4 Molecular Docking
11.3.5 Molecular Dynamics Simulations
11.4 Applications
11.4.1 Kinase Inhibitors
11.4.2 Enzyme Inhibitors
11.4.3 G Protein-Coupled Receptor (GPCR) Ligands
11.4.4 Antibodies and Vaccines
11.5 Other Examples
11.5.1 Anti-Cancer Drugs
11.5.2 Anti-Viral Drugs
11.5.3 Neurological Disorders
11.5.4 Cardiovascular Diseases
11.6 Advantages and Limitations of a Structure-Based Drug Design
11.6.1 Advantages of Rational Drug Design
11.6.2 Limitations and Challenges
11.6.3 Comparison with Other Drug Discovery Methods
11.7 Case Studies and Examples
11.7.1 Discovery of HIV Protease Inhibitors
11.7.2 Development of HER2 Kinase Inhibitors
11.7.3 Design of Influenza Neuraminidase Inhibitors
11.7.4 Other Examples of Successful Structure-Based Drug Design
11.8 Future Outlook and Implications
11.8.1 Emerging Trends and Technologies
11.8.2 Integration with Other Drug Discovery Methods
11.9 Potential Impact on Healthcare and Drug Development
11.9.1 Research Directions
11.10 Conclusion and Future Scope
References
12. AI-Based Personalized Drug Treatment
Shanmuga Sundari M., Harshini Reddy Penthala, Akshita Mogullapalli and Mayukha Mandya Ammangatambu
12.1 Introduction
12.1.1 Background on Personalized Medicine
12.1.2 Overview of AI in Drug Treatment
12.1.3 Importance of Personalized Drug Treatment
12.2 How AI Can Improve Drug Treatment?
12.2.1 Predicting Drug Efficacy
12.2.2 Identifying Patient Subgroups
12.2.3 Tailoring Dosages
12.2.4 Minimizing Adverse Effects
12.2.5 Streamlining Drug Development
12.3 Techniques Used in AI-Based Drug Treatment
12.3.1 Machine Learning in AI-Based Drug Treatment
12.3.2 Natural Language Processing (NLP) in AI-Based Drug Treatment
12.3.3 Deep Learning in AI-Based Drug Treatment
12.3.4 Other AI Techniques Used in AI-Based Drug Development
12.3.4.1 Reinforcement Learning
12.3.4.2 Bayesian Networks
12.3.4.3 Genetic Algorithms
12.3.4.4 Expert Systems
12.4 Case Studies and Examples
12.4.1 Genomic Health’s Oncotype DX
12.4.2 IBM Watson for Drug Discovery
12.4.3 BenevolentAI’s Drug Discovery Platform
12.4.4 Other Examples of AI-Based Drug Treatment
12.5 Challenges and Limitations of AI-Based Drug Treatment
12.5.1 Data Quality and Availability
12.5.2 Ethical Considerations
12.5.3 Regulatory Hurdles
12.5.4 Integration with Existing Healthcare Systems
12.6 Future Outlook and Implications
12.6.1 Potential Impact on Healthcare
12.6.2 Opportunities for Research and Development
12.6.3 Implications for Patients and Providers
12.7 Conclusion and Future Work
12.7.1 Summary of Key Points
12.7.2 Implications for the Future of Drug Treatment
12.7.3 Further Research and Development
References
13. AI Models for Biopharmaceutical Property Prediction
Bancha Yingngam
List of Abbreviations
13.1 Introduction
13.2 AI Models for Biopharmaceutical Property Prediction
13.2.1 Types of ML Models for Biopharmaceutical Property Prediction
13.2.1.1 Supervised Learning Models
13.2.1.2 Unsupervised Learning Models
13.2.2 Data Sources for AI Models
13.2.3 Popularly Used Software to Develop AI Models
13.2.3.1 SAS
13.2.3.2 MATLAB
13.2.3.3 IBM Watson
13.2.3.4 Accelrys
13.2.4 Applications of AI Models for Predicting Specific Biopharmaceutical Properties
13.2.4.1 Drug Solubility Prediction
13.2.4.2 Protein‒Ligand Binding Affinity Prediction
13.2.4.3 Stability Prediction
13.2.4.4 Aggregation Prediction
13.2.4.5 Immunogenicity Prediction
13.3 Recent Advances in AI Models for Biopharmaceutical Property Prediction
13.3.1 Deep Learning and Ensemble Models
13.3.2 Comparison of Different AI Models
13.3.2.1 Schrödinger’s Maestro Suite
13.3.2.2 Biovia’s Discovery Studio
13.3.2.3 Genedata’s Screener
13.3.3 Successful Application of AI Models
13.3.3.1 G Protein-Coupled Receptor Modeling
13.3.3.2 Protein‒Ligand Binding
13.3.3.3 Pharmacokinetics Modeling
13.3.3.4 Toxicity Prediction
13.3.3.5 Drug‒Drug Interaction Prediction
13.4 Case Study: COVID-19 Vaccines
13.4.1 Identifying Vaccine Targets
13.4.2 Designing Vaccine Candidates
13.4.3 Predicting Vaccine Efficacy
13.4.4 Optimizing Vaccine Formulations
13.4.5 Accelerating Clinical Trials
13.5 Current Research in Applications of AI for Biopharmaceuticals
13.6 Future Directions and Challenges
13.6.1 Potential Impact of AI Models
13.6.2 Potential Challenges and Limitations of AI Models
13.6.2.1 Data Quality and Bias
13.6.2.2 Interpretability
13.6.2.3 Overfitting and Underfitting
13.6.2.4 Generalizability
13.6.2.5 Ethical Considerations
13.6.3 Areas for Future Research and Development in the Field
13.6.3.1 Multitask Learning
13.6.3.2 Incorporating Biological Data
13.6.3.3 Improving Interpretability
13.6.3.4 Transfer Learning
13.6.3.5 Addressing Ethical Concerns
13.7 Conclusion and Future Scope
References
14. Deep Learning Tactics for Neuroimaging Genomics Investigations in Alzheimer’s Disease
Mithun Singh Rajput, Jigna Shah, Viral Patel, Nitin Singh Rajput and Dileep Kumar
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
14.4.1 Deep Learning-Based Fragmentation of Genome Data
14.4.2 Phenotype Classification Using Deep Learning
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
Raghunath Satpathy
15.1 Introduction
15.2 Overview of the Drug Design Process
15.2.1 Target Identification
15.2.2 Target Characterization
15.2.3 Hit Identification
15.2.4 Hit-to-Lead Phase and Lead Optimization
15.2.5 Preclinical and Clinical Development
15.3 Computational Tools and Techniques in CADD
15.3.1 Homology Modeling
15.3.2 Molecular Docking
15.3.3 Molecular Dynamics Simulation
15.3.4 QSAR Modeling
15.4 Concept of Artificial Intelligence (AI) and Machine Learning (ML) Methods
15.4.1 Basic Steps Involved in the Implementation of AI Techniques in the Compound Screening Process
15.4.1.1 Searching Molecular Information from the Public Data Resources
15.4.1.2 Small Molecule Representation Format
15.5 Major Machine Learning (ML) Techniques and Applications in Molecular Screening Process
15.5.1 Naive Bayes
15.5.2 Support Vector Machines (SVM)
15.5.3 Random Forest (RF)
15.5.4 Artificial Neural Networks (ANN)
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
Devi Rajeev, Remya S. and Anand Nayyar
16.1 Introduction
16.1.1 Overview and Significance of Liver Transplantation
16.1.2 The Significance of Efficient Clinical Decision-Making in Liver Transplantation
16.1.3 The Role of AI-Based Scoring Methods in Enhancing Efficiency in Liver Transplantation Decision-Making
16.2 Review Methodology
16.2.1 Inclusion Criteria
16.2.2 Selecting Research Literature for a Comprehensive Systematic Review on AI-Driven Scoring Methods in Liver Transplantation Decision-Making
16.3 A Comprehensive Literature Review of AI-Driven Scoring Methods for Predicting Liver Transplantation Outcomes
16.3.1 AI-Driven Methods Used for the Prediction of Liver Transplantation
16.3.1.1 Workflow of AI-Driven Methods in Liver Transplantation Prediction: Enhancing Clinical Decision-Making
16.3.1.2 Decision Trees
16.3.1.3 Random Forest
16.3.1.4 Support Vector Machines (SVM)
16.3.1.5 Neural Networks
16.3.1.6 Deep Learning
16.3.1.7 Potential of AI in Enhancing Liver Transplantation Decision-Making
16.3.2 Integration of Clinical Tools with AI Algorithms for Enhanced Liver Transplantation Prediction: Comments and Observations
16.3.2.1 MELD Score with AI Algorithms
16.3.2.2 CHILD Score with AI Algorithms
16.3.2.3 Donor Risk Index (DRI) Score with AI Algorithms
16.3.2.4 Donor–Recipient Matching (DRM) Score with AI Algorithms
16.3.2.5 MILAN Score with AI Algorithms
16.3.2.6 UCSF Score with AI Algorithms
16.3.2.7 Up to Seven Score with AI Algorithms
16.3.3 Assessing the Efficacy of AI-Based Scoring Techniques in Enhancing Clinical Decision-Making for Liver Transplantation
16.3.4 The Promise of AI Integration with Clinical Scoring Methods in Liver Transplantation Decision-Making
16.4 Discussion and Insights
16.4.1 Parameters in Clinical Scoring Methods
16.4.2 Assessment of Clinical Scoring Accuracy Using Diverse AI Algorithms
16.4.3 Strengths and Limitations of AI-Based Scoring Methods
16.4.4 Implications of the Findings for Improving Clinical Decision-Making in Liver Transplantation
16.5 Conclusion and Future Scope
References
17. Pushing Boundaries: The Landscape of AI-Driven Drug Discovery and Development with Insights Into Regulatory Aspects
Dipak D. Gadade, Deepak A. Kulkarni, Ravi Raj, Swapnil G. Patil and Anuj Modi
17.1 Introduction
17.1.1 AI for DDS and DVPT
17.2 Classification of AI
17.2.1 Narrow AI
17.2.2 General AI
17.2.3 Super AI
17.3 Overview of AI Technologies Used in DDS
17.3.1 Machine Learning (ML)
17.3.2 Deep Learning (DL)
17.3.3 Natural Language Processing (NLP)
17.3.4 Robotics
17.3.5 Fuzzy Logic
17.3.6 Swarm Intelligence
17.3.7 Reactive Machines
17.4 Applications of AI in DDS and Drug DVPT
17.4.1 Preclinical Studies and Safety Testing
17.4.2 Target Identification and Validation
17.4.3 Hit Identification and Lead Optimization
17.4.4 Prediction of Drug Efficacy and Toxicity
17.4.5 Design of Clinical Trials
17.5 Ethical Considerations Regarding the Use of AI in DDS and DVPT
17.5.1 Data Privacy
17.5.1.1 Informed Consent
17.5.1.2 Anonymization
17.5.1.3 Data Security
17.5.1.4 Data Governance
17.5.2 Bias
17.5.2.1 Target Identification
17.5.2.2 Compound Screening
17.5.2.3 Clinical Trials
17.5.3 Transparency
17.5.3.1 Ethical Implications of Opaque AI Systems
17.5.3.2 Importance of Transparency in AI-Based DDS and DVPT
17.5.4 Accountability
17.5.4.1 Importance of Accountability
17.5.4.2 Challenges in Maintaining Accountability
17.5.5 Safety–Efficacy
17.5.5.1 Safety–Efficacy Balance in AI-Based DDS and DVPT
17.5.5.2 Ethical Considerations Related to the Safety–Efficacy Balance in AI-Generated Drugs
17.6 IPR Issues
17.6.1 Patentability of AI-Generated Inventions
17.6.2 Ownership and Licensing of AI-Generated Inventions
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
Rahul Joshi and Rhythma Badola
18.1 Introduction
18.1.1 Foundations of AI
18.1.2 Objectives of AI
18.1.3 Healthcare and AI
18.1.4 Latest Trends in Technology
18.1.4.1 Latest Trends in Health Industry
18.1.5 Limitations of AI in Healthcare
18.2 Robotics and Their Types in Healthcare
18.2.1 Types of Robotics
18.2.1.1 Surgical Robotics
18.2.1.2 Rehabilitation Robotics
18.2.1.3 Socially Assistive Robotics
18.2.1.4 Non-Medical Robots
18.3 Pros of Robotics in Healthcare
18.4 Insights Into Robotic Surgeries in India
18.4.1 Robotics Inclusion in India: Gaining Popularity
18.4.1.1 Conditions Treated by Robotic Surgeries in India
18.4.2 Strengths of Robotic Surgeries in India
18.4.3 Opportunities for Robotic Surgeries in India
18.4.4 Real-Time Success Stories of Robotics Utilization in Indian Hospitals
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
Wasswa Shafik
19.1 Introduction
19.1.1 Artificial Intelligence and Machine Learning Overview
19.1.2 Artificial Intelligence and Machine Learning from a Medical Perspective
19.2 Application of AI and ML in Drug Design and Development
19.2.1 AI in Drug Design and Development
19.2.1.1 Predictive Modeling
19.2.1.2 Drug Repurposing
19.2.1.3 Clinical Trial Optimization
19.2.1.4 Personalized Medicine
19.2.1.5 Drug Safety and Toxicity Prediction
19.2.1.6 Virtual Screening
19.2.1.7 De Novo Drug Design
19.2.1.8 Biomarker Discovery
19.2.1.9 Protein Structure Prediction
19.2.1.10 Pharmacokinetic Modeling
19.2.1.11 Adverse Event Prediction
19.2.1.12 Natural Product Discovery
19.2.1.13 Drug Formulation Optimization
19.2.1.14 Quality Control
19.2.1.15 Regulatory Compliance
19.2.2 Application of ML in Drug Design and Development
19.2.2.1 Virtual Drug Screening
19.2.2.2 De Novo Drug Design and Development
19.2.2.3 Biomarker Detection
19.2.2.4 Scientific Trial Optimization
19.2.2.5 Patient Mobile Medicine
19.2.2.6 Toxicity Prediction
19.2.2.7 Protein Structure Prediction
19.2.2.8 Drug Repurposing
19.2.2.9 Quality Controller
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
19.6.1 Early Detection of Diabetic Retinopathy
19.6.2 Predictive Analytics for Patient Outcomes
19.6.3 Personalized Treatment Recommendations
19.6.4 Chatbots for Mental Health
19.6.5 Early Detection of Alzheimer’s Disease
19.7 Conclusion and Future Scope
References
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