book

Artificial Intelligence and Machine Learning in Drug Design and Development

Name: Artificial Intelligence and Machine Learning in Drug Design and Development
ISBN: 9781394234165

by Abhirup Khanna, May El Barachi, Sapna Jain, Manoj Kumar, Anand Nayyar

July 2024

Intermediate to advanced

672 pages

25h 27m

English

Wiley-Scrivener

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Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 The Rise of Intelligent Machines: An Introduction to Artificial Intelligence
1.1 Introduction1.2 Key Components of Artificial Intelligence1.3 Applications of Artificial Intelligence1.4 Generative AI1.5 Ethical and Societal Implications of AI1.6 Ethical AI Development1.7 Future of AI1.8 ConclusionReferences
2 Introduction to Bioinformatics
List of Abbreviations2.1 Introduction2.2 Key Concepts in Bioinformatics2.3 Bioinformatics Tools and Databases2.4 Applications in Bioinformatics2.5 Challenges and Opportunities in Bioinformatics2.6 Future Directions of Bioinformatics in Drug Design2.7 Conclusion and Future ScopeReferences
3 Exploring the Intersection of Biology and Computing: Road Ahead to Bioinformatics
3.1 Introduction3.2 Bioinformatics in Systems Biology3.3 Tools and Techniques in Bioinformatics3.4 Bioinformatics in Precision Medicine3.5 Challenges in Bioinformatics3.6 Research Directions3.7 Conclusion and Future ScopeFuture Scope and Potential OpportunitiesReferences
4 Machine Learning in Drug Discovery: Methods, Applications, and Challenges
4.1 Introduction4.2 Applications of AI and ML in Drug Discovery4.3 AI and ML Methods to Drug Discovery4.4 Challenges4.5 Conclusion and Future DirectionsReferences

5 Artificial Intelligence for Understanding Mechanisms of Antimicrobial Resistance and Antimicrobial Discovery: A New Age Model for Translational Research
5.1 Introduction5.2 Commonly Used Artificial Intelligence Algorithms for AMR5.3 AI for Understanding Mechanisms of AMR and Antimicrobial Discovery5.4 Strategies to Overcome Antibiotic Resistance5.5 Applications of Artificial Intelligence for Antimicrobial Resistance5.6 Challenges Towards Practical Implementation5.7 Conclusion and Future ScopeReferences
6 Artificial Intelligence-Powered Molecular Docking: A Promising Tool for Rational Drug Design
6.1 Introduction6.2 Basics of Molecular Docking6.3 Role of Artificial Intelligence in Molecular Docking6.4 Drug Discovery in the New Age6.5 Drug Discovery Using Machine Learning (ML) Algorithms6.6 Drug Discovery Using Deep Learning (DL) Algorithms6.7 AI-Based Toolkits Used for Drug Discovery6.8 Applications of AI in Molecular Docking6.9 Challenges and Limitations of AI-Based Molecular Docking6.10 Conclusion and Future ScopeFuture DirectionsReferences
7 Revolutionizing Drug Discovery: The Roleof AI and Machine Learning in Accelerating Medicinal Advancements
7.1 Introduction7.2 Machine Learning Techniques in Drug Discovery7.3 AI Techniques for Prediction and Analysis of Drugs7.4 AI for Revolutionizing Drug Development7.5 Challenges and Solutions7.6 Conclusion and Future ScopeFuture ScopeReferences
8 Data Processing Method for AI-Driven Predictive Models for CNS Drug Discovery
8.1 Introduction8.2 The Role of AI And ML in Drug Discovery8.3 Role of AI and ML in Central Nervous System (CNS)8.4 The Effect of AI/ML on CNS Drug Research8.5 Prospects for AI/ML in CNS Drug Research in Future8.6 Proposed Methodology on Data Processing for CNS Drug-Likeness Prediction8.7 Conclusion and Future ScopeReferences
9 Machine Learning Applications for Drug Repurposing
List of Abbreviations9.1 Introduction9.2 Trends in ML Applications for Drug Repurposing9.3 Understanding Drug Repurposing9.4 Traditional Techniques in Drug Repurposing9.5 Modern Technologies in Drug Repurposing9.6 Data Sources for Drug Repurposing9.7 Case Studies: Applications of Machine Learning in Drug Repurposing9.8 Future of Machine Learning in Drug Repurposing9.9 Conclusion and Future ScopeReferences
10 Personalized Drug Treatment: Transforming Healthcare with AI
10.1 Introduction10.2 Cheminformatics10.3 Data Sources10.4 Precision Medicine vs. Personalized Drug Treatment10.5 AI Models for Healthcare10.6 Ethical Considerations in AI-Enabled Personalized Drug Treatment10.7 Benefits and Limitations of AI-Enabled Personalized Drug Treatment10.8 Case Studies10.9 Conclusion, Challenges, and OpportunitiesReferences
11 Process and Applications of Structure-Based Drug Design
11.1 Introduction11.2 Structure-Based Drug Design: Steps11.3 Tools and Techniques Used in Structure-Based Drug Design11.4 Applications11.5 Other Examples11.6 Advantages and Limitations of a Structure-Based Drug Design11.7 Case Studies and Examples11.8 Future Outlook and Implications11.9 Potential Impact on Healthcare and Drug Development11.10 Conclusion and Future ScopeReferences
12 AI-Based Personalized Drug Treatment
12.1 Introduction12.2 How AI Can Improve Drug Treatment?12.3 Techniques Used in AI-Based Drug Treatment12.4 Case Studies and Examples12.5 Challenges and Limitations of AI-Based Drug Treatment12.6 Future Outlook and Implications12.7 Conclusion and Future WorkReferences
13 AI Models for Biopharmaceutical Property Prediction
List of Abbreviations13.1 Introduction13.2 AI Models for Biopharmaceutical Property Prediction13.3 Recent Advances in AI Models for Biopharmaceutical Property Prediction13.4 Case Study: COVID-19 Vaccines13.5 Current Research in Applications of AI for Biopharmaceuticals13.6 Future Directions and Challenges13.7 Conclusion and Future ScopeReferences
14 Deep Learning Tactics for Neuroimaging Genomics Investigations in Alzheimer’s Disease
14.1 Introduction14.2 Pathophysiology of Alzheimer’s Disease14.3 Deep Learning Tactics in the Prediction, Classification, and Diagnosis of AD14.4 Deep Learning-Based Identification of Genetic Variants14.5 Deep Learning-Based Prediction of Altered Genes and mRNA Levels in AD14.6 Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease14.7 Limitations and Challenges in Deep Learning-Based Neuroimaging Genomics Investigations in Alzheimer’s Disease14.8 Future Prospects for Applying Deep Learning Techniques in Alzheimer’s Disease Treatment Environments14.9 Conclusion and Future ScopeReferences
15 Artificial Intelligence Techniques in the Classification and Screening of Compounds in Computer-Aided Drug Design (CADD) Process
15.1 Introduction15.2 Overview of the Drug Design Process15.3 Computational Tools and Techniques in CADD15.4 Concept of Artificial Intelligence (AI) and Machine Learning (ML) Methods15.5 Major Machine Learning (ML) Techniques and Applications in Molecular Screening Process15.6 Challenges and Opportunities15.7 Conclusion and Future PerspectivesReferences
16 Empowering Clinical Decision Making: An In-Depth Systematic Review of AI-Driven Scoring Approaches for Liver Transplantation Prediction
16.1 Introduction16.2 Review Methodology16.3 A Comprehensive Literature Review of AI-Driven Scoring Methods for Predicting Liver Transplantation Outcomes16.4 Discussion and Insights16.5 Conclusion and Future ScopeReferences
17 Pushing Boundaries: The Landscape of AI-Driven Drug Discovery and Development with Insights Into Regulatory Aspects
17.1 Introduction17.2 Classification of AI17.3 Overview of AI Technologies Used in DDS17.4 Applications of AI in DDS and Drug DVPT17.5 Ethical Considerations Regarding the Use of AI in DDS and DVPT17.6 IPR Issues17.7 Regulatory Approval and Market Access17.8 AI in Medicine Current DVPTs and Strategy for Pharmaceutical Companies17.9 Conclusion and Future PerspectivesReferences
18 Feasibility of AI and Robotics in Indian Healthcare: A Narrative Analysis
18.1 Introduction18.2 Robotics and Their Types in Healthcare18.3 Pros of Robotics in Healthcare18.4 Insights Into Robotic Surgeries in India18.5 Limitations of Robotics in Healthcare18.6 Future Applications of Robotics and AI18.7 Conclusion and Future ScopeFuture ScopeReferences
19 The Future of Healthcare: AIoMT—Redefining Healthcare with Advanced Artificial Intelligence and Machine Learning Techniques
19.1 Introduction19.2 Application of AI and ML in Drug Design and Development19.3 Secure AIoMT Framework for Smart Healthcare19.4 AIoMT Cybersecurity Aspects19.5 AIoMT Threats, Attacks, and Countermeasures19.6 Selected Case Studies19.7 Conclusion and Future ScopeReferences
Index
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Content preview from Artificial Intelligence and Machine Learning in Drug Design and Development

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

Devi Rajeev¹, Remya S.¹ and Anand Nayyar²*

¹School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, India

²School of Computer Science, Duy Tan University, Da Nang, Viet Nam

Abstract

Effective clinical decision-making is critical in liver transplantation, as timely and precise assessments substantially influence patient outcomes. The chapter examine the possible benefits of artificial intelligence (AI) tools in improving clinical decision-making in liver transplantation. As part of this research, 44 relevant research papers are analyzed that satisfied the inclusion requirements. Various AI methodologies in liver transplantation, such as machine learning, deep learning, and predictive modeling are examined. This study aimed to assess whether AI-based scoring algorithms can improve the accuracy and efficiency of clinical judgments and predict outcomes such as graft failure, patient survival, and rejection. The findings suggest that AI-based models can improve clinical decision-making by providing accurate forecasts of critical outcomes and expediting evaluations, resulting in timely interventions. However, successfully integrating AI into clinical practice requires further research and validation. These insights benefit doctors, researchers, and policymakers interested in leveraging AI to enhance decision-making efficiency in liver ...

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The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry

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Artificial Intelligence and Machine Learning in Drug Design and Development

by Abhirup Khanna, May El Barachi, Sapna Jain, Manoj Kumar, Anand Nayyar