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Introduction

In recent years, the field of drug discovery has experienced a rapid evolution due to advancements in artificial intelligence (AI) technology. AI systems have shown great potential in accelerating the drug discovery process by predicting drug-target interactions, identifying novel drug candidates, and optimizing drug design. The integration of AI into drug discovery has the potential to revolutionize the pharmaceutical industry by reducing costs, time, and resources required for the development of new therapies.

This thesis aims to explore the development of an AI system for drug discovery, with a focus on leveraging machine learning algorithms to predict drug-target interactions. By harnessing the power of AI, this system seeks to streamline the drug discovery process and expedite the identification of potential drug candidates 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 Overview of AI in drug discovery
2.2 Machine learning algorithms in drug discovery
2.3 Applications of AI in drug design
2.4 Challenges in AI-driven drug discovery
2.5 Integration of omics data in drug discovery
2.6 AI-based drug repurposing
2.7 Ethical considerations in AI-driven drug discovery
2.8 Success stories of AI in drug discovery
2.9 Future directions of AI in drug discovery
2.10 Comparison of AI vs traditional methods in drug discovery

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Model selection and evaluation
3.4 Cross-validation and hyperparameter tuning
3.5 Ensemble learning techniques
3.6 Interpretability and explainability of AI models
3.7 Performance metrics and validation techniques
3.8 Software and tools used in system development

Chapter 4: System Implementation
4.1 System architecture and components
4.2 Database design and management
4.3 Model deployment and integration
4.4 User interface design
4.5 Testing and validation procedures
4.6 Scalability and performance optimization
4.7 Security and privacy considerations
4.8 Maintenance and support strategies

Chapter 5: Conclusion and Summary
In conclusion, this thesis aims to contribute to the field of drug discovery by developing an AI system that can accurately predict drug-target interactions. By leveraging machine learning algorithms and omics data, this system has the potential to revolutionize the drug discovery process and significantly impact the pharmaceutical industry. Through the systematic development and implementation of this AI system, we hope to accelerate the pace of drug discovery and ultimately improve patient outcomes.

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