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Introduction

In recent years, the field of drug discovery has seen a significant shift towards utilizing advanced technologies such as machine learning to identify new uses for existing drugs, a process known as drug repurposing. Quantum machine learning, a novel approach that combines quantum computing with machine learning algorithms, has shown promise in accelerating the drug repurposing process by efficiently analyzing massive amounts of data and identifying potential drug candidates with higher accuracy and speed.

This thesis aims to explore the potential applications of quantum machine learning in drug repurposing, addressing the challenges and limitations of traditional methods and proposing innovative solutions to improve drug discovery outcomes. By leveraging the power of quantum computing, this research seeks to revolutionize the way drugs are discovered and repurposed, ultimately leading to faster and more cost-effective treatments for various diseases.

Chapter 1: Introduction
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 2: Literature Review
2.1 Traditional drug discovery methods
2.2 Drug repurposing approaches
2.3 Quantum computing basics
2.4 Machine learning in drug discovery
2.5 Quantum machine learning applications
2.6 Challenges in drug repurposing
2.7 Quantum machine learning algorithms
2.8 Current trends in drug repurposing
2.9 Quantum machine learning in healthcare
2.10 Future prospects of quantum machine learning in drug repurposing

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Quantum machine learning models
3.4 Experimental setup
3.5 Data preprocessing techniques
3.6 Performance evaluation metrics
3.7 Validation methods
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of results
4.2 Comparison with existing methods
4.3 Interpretation of findings
4.4 Implications for drug discovery
4.5 Recommendations for future research
4.6 Limitations of the study
4.7 Practical implications
4.8 Theoretical contributions

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Conclusion
5.3 Contributions to the field
5.4 Implications for practice
5.5 Future research directions

Thesis Overview on Quantum machine learning for drug repurposing

The field of drug repurposing has gained significant attention in recent years as a cost-effective and time-saving approach to drug discovery. By identifying new uses for existing drugs, researchers can bypass the lengthy and expensive process of developing new drugs from scratch. However, traditional methods of drug repurposing are limited by the complexity and size of the data sets involved, making it challenging to identify novel drug candidates efficiently.

Quantum machine learning, a cutting-edge technology that combines quantum computing with machine learning algorithms, offers a new approach to drug repurposing that promises to revolutionize the field. By leveraging the unique properties of quantum computing, such as superposition and entanglement, quantum machine learning can process vast amounts of data simultaneously and identify patterns and correlations that traditional methods may overlook.

This thesis aims to explore the potential applications of quantum machine learning in drug repurposing, addressing the challenges and limitations of existing methods and proposing innovative solutions to improve drug discovery outcomes. Through a comprehensive literature review, research methodology, and analysis of findings, this research seeks to demonstrate the effectiveness of quantum machine learning in accelerating the drug repurposing process and improving patient outcomes.

By harnessing the power of quantum computing and machine learning, this research aims to pave the way for a new era of drug discovery that is faster, more accurate, and ultimately more successful in bringing life-saving treatments to patients worldwide.

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