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Introduction

Quantum computing has emerged as a promising technology with the potential to revolutionize the field of optimization. In particular, the application of quantum computing in drug discovery has gained significant attention in recent years. Traditional methods of optimization in drug discovery often face challenges related to the complexity and scale of the computational problems involved. Quantum computing offers the possibility of solving these challenges by harnessing the principles of quantum mechanics to perform computations at a speed and scale that are beyond the capabilities of classical computers.

This thesis aims to explore the potential of quantum computing for optimization problems in drug discovery. By leveraging the unique properties of quantum systems, such as superposition and entanglement, quantum algorithms have the potential to significantly improve the efficiency and accuracy of optimization techniques in drug discovery. This research seeks to investigate the application of quantum computing in addressing key challenges in drug discovery, such as molecular structure optimization, drug design, and protein-ligand binding affinity prediction.

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 Overview of Quantum Computing
2.2 Applications of Quantum Computing in Optimization
2.3 Quantum Algorithms for Optimization Problems
2.4 Drug Discovery: Challenges and Opportunities
2.5 Traditional Optimization Methods in Drug Discovery
2.6 Quantum Computing in Drug Discovery: State of the Art
2.7 Quantum Computing Platforms and Technologies
2.8 Case Studies on Quantum Computing in Drug Discovery
2.9 Limitations and Challenges of Quantum Computing in Drug Discovery
2.10 Future Directions and Opportunities

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Quantum Computing Simulation
3.4 Optimization Algorithms
3.5 Case Study Design
3.6 Data Analysis
3.7 Validation and Verification
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Molecular Structure Optimization
4.2 Drug Design
4.3 Protein-Ligand Binding Affinity Prediction
4.4 Comparison of Quantum and Classical Methods
4.5 Challenges and Limitations
4.6 Implications for Drug Discovery
4.7 Recommendations for Future Research
4.8 Practical Applications and Industry Impact

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Drug Discovery
5.3 Contribution to the Field
5.4 Limitations and Future Directions
5.5 Conclusion

In conclusion, this thesis aims to provide a comprehensive exploration of the potential of quantum computing for optimization problems in drug discovery. By analyzing the current state of the art, identifying key challenges, and proposing novel solutions, this research seeks to advance our understanding of the role of quantum computing in revolutionizing drug discovery. By bridging the gap between theory and practice, this thesis aims to make a significant contribution to the field of computational chemistry and pharmaceutical research.

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