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Introduction

The field of drug discovery and development is a complex and time-consuming process that often involves significant financial investments. One approach to expedite this process is drug repurposing, which involves finding new therapeutic indications for existing drugs. In recent years, artificial intelligence (AI) has emerged as a powerful tool in drug repurposing, offering new possibilities for identifying novel uses for existing drugs.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objectives of study
1.5 Limitations of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of drug repurposing
2.2 Role of AI in drug repurposing
2.3 Applications of AI in drug discovery
2.4 Challenges in drug repurposing using AI
2.5 Case studies of successful AI-driven drug repurposing
2.6 Comparison of AI vs traditional drug repurposing methods
2.7 Regulatory considerations in AI-driven drug repurposing
2.8 Ethical considerations in AI-driven drug repurposing
2.9 Future directions in AI-driven drug repurposing

Chapter 3: Research Methodology
3.1 Data collection methods
3.2 Data analysis techniques
3.3 AI algorithms used in drug repurposing
3.4 Study design
3.5 Population/sample selection
3.6 Variables and measurements
3.7 Data validation techniques
3.8 Statistical analysis methods

Chapter 4: Discussion of Findings
4.1 Summary of findings
4.2 Analysis of results
4.3 Comparison with existing literature
4.4 Implications of findings
4.5 Limitations of the study
4.6 Recommendations for future research
4.7 Practical applications of the research
4.8 Contribution to the field of drug repurposing

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications of the study
5.3 Contributions to the field
5.4 Limitations of the research
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview on AI-driven Drug Repurposing

Drug repurposing, also known as drug repositioning or drug reprofiling, is the process of identifying new therapeutic uses for existing drugs that are already approved for other indications. This approach offers several advantages over traditional drug discovery methods, including reduced development time, lower costs, and decreased risks associated with safety and toxicity.

Artificial intelligence (AI) has revolutionized the field of drug discovery and development, offering new possibilities for identifying novel uses for existing drugs. By leveraging advanced machine learning algorithms and big data analysis, AI-driven drug repurposing holds the potential to accelerate the drug development process and improve patient outcomes.

This thesis aims to explore the role of AI in drug repurposing, examining the current state of the field, challenges and opportunities, and future directions. Through a comprehensive literature review, research methodology, and discussion of findings, this study seeks to contribute to the growing body of knowledge on AI-driven drug repurposing and its implications for the pharmaceutical industry.

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