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Introduction:

In recent years, the use of artificial intelligence (AI) and machine learning (ML) has revolutionized various industries including healthcare. One of the promising applications of AI and ML in healthcare is drug repurposing and drug repositioning. This research aims to explore the potential of AI and ML techniques in identifying new therapeutic uses for existing drugs, which can significantly reduce the time and cost associated with traditional drug development.

Chapter One:
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter Two: Literature Review
2.1 Overview of drug repurposing and drug repositioning
2.2 Traditional methods of drug discovery
2.3 Role of AI and ML in drug discovery
2.4 Case studies of successful drug repurposing/repositioning
2.5 Challenges and limitations in using AI and ML for drug discovery
2.6 Ethical considerations in drug repurposing/repositioning
2.7 Regulatory guidelines for drug repurposing/repositioning
2.8 Future directions in AI and ML for drug discovery
2.9 Comparison of AI and ML techniques for drug repurposing/repositioning
2.10 Summary of key findings from literature review

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Study population and sample size
3.5 AI and ML algorithms used
3.6 Model validation techniques
3.7 Software and tools used
3.8 Ethical considerations
3.9 Limitations of the research methodology

Chapter Four: Discussion of Findings
4.1 Overview of the research findings
4.2 Comparison with existing literature
4.3 Implications of the findings
4.4 Recommendations for future research
4.5 Strengths and limitations of the study
4.6 Practical implications for drug discovery industry
4.7 Policy recommendations
4.8 Suggestions for further research

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusions drawn from the research
5.3 Contribution to the field of drug repurposing/repositioning
5.4 Recommendations for practitioners and policymakers
5.5 Limitations of the study
5.6 Suggestions for future research

Thesis Overview:

The use of artificial intelligence and machine learning in drug repurposing and drug repositioning is a cutting-edge research area that has gained significant interest in the pharmaceutical industry. This thesis aims to investigate the potential of AI and ML techniques in identifying new therapeutic uses for existing drugs, thereby accelerating the drug discovery process.

Chapter one provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter two offers a comprehensive literature review on drug repurposing and drug repositioning, traditional methods of drug discovery, the role of AI and ML, case studies, challenges, ethical considerations, regulatory guidelines, and future directions.

Chapter three details the research methodology, including research design, data collection, analysis, study population, AI and ML algorithms, model validation, software tools, ethical considerations, and limitations. Chapter four discusses the findings of the research, compares them with existing literature, examines implications, makes recommendations, and considers strengths and limitations.

Chapter five concludes the thesis by summarizing key findings, drawing conclusions, assessing contributions to the field, recommending actions for practitioners and policymakers, addressing limitations, and suggesting areas for future research. Through this comprehensive exploration of AI and ML in drug repurposing and drug repositioning, this thesis aims to advance knowledge in the field and offer valuable insights to researchers, industry professionals, regulators, and policymakers.

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