Prompt Title: AI in Drug Discovery.

Title: A Literature Review on Artificial Intelligence in Drug Discovery (2015-2021): A Focus on Molecular Docking, Target Prediction, and Drug Repurposing

Abstract: This literature review explores the role of artificial intelligence (AI) in drug discovery, focusing on molecular docking, target prediction, and drug repurposing from 2015 to 2021. The review analyzes the relationship between AI techniques and drug discovery stages, trends in AI advancements and their adoption in the pharmaceutical industry, and the methodologies employed in computational models and validation methods. Additionally, this review raises research questions regarding the future integration of AI in drug discovery and the ethical considerations of such advancements.

1. Introduction The application of AI in drug discovery has grown significantly over the past decade, with promising advancements in molecular docking, target prediction, and drug repurposing. This literature review aims to provide a comprehensive overview of these subtopics and the methodologies employed in computational models and validation methods. Furthermore, the review identifies trends in AI advancements and their adoption in the pharmaceutical industry, and discusses potential future AI integration and ethical considerations.

2. AI in Molecular Docking Molecular docking is a vital step in drug discovery, aiming to predict the preferred orientation of a molecule when bound to a protein target. The application of AI techniques in molecular docking has led to enhanced accuracy, efficiency, and the discovery of novel compounds. In this section, we review the most recent AI-based molecular docking techniques, including deep learning, reinforcement learning, and swarm intelligence algorithms.

3. AI in Target Prediction Target prediction involves identifying the molecular targets of bioactive compounds, which is crucial for understanding the mechanism of action and potential side effects of drugs. AI techniques, including machine learning and neural networks, have significantly improved target prediction accuracy, specificity, and sensitivity. This section covers the key AI-driven advancements in target prediction, including multi-target prediction, chemical similarity-based methods, and data mining techniques.

4. AI in Drug Repurposing Drug repurposing aims to find new uses for existing drugs, thus saving time and resources compared to de novo drug discovery. AI has enabled the rapid identification of drug repurposing candidates by analyzing large-scale data sets and predicting drug-target interactions. This section discusses the most notable AI-powered drug repurposing techniques, such as machine learning algorithms, network-based approaches, and natural language processing methods.

5. Methodologies: Computational Models and Validation Methods This section provides an in-depth analysis of the computational models and validation methods employed in AI-based drug discovery research. We focus on the importance of data preprocessing, feature selection, model training, and validation in developing accurate and robust AI models. Additionally, we discuss the challenges and limitations associated with these methodologies.

6. Trend Analysis: AI Advancements and Adoption in the Pharmaceutical Industry The growing interest in AI-driven drug discovery has led to increased collaboration between academia, biotech companies, and pharmaceutical firms. This section analyzes the trends in AI advancements and their adoption in the pharmaceutical industry, highlighting the factors contributing to this growth and the challenges faced in integrating AI into the drug discovery process.

7. Research Questions and Ethical Considerations As AI continues to revolutionize drug discovery, several research questions and ethical considerations arise. This section explores potential future AI integration in drug discovery, addressing the potential risks and benefits, the role of AI in personalized medicine, and the need for transparency and accountability in AI-driven research. Additionally, we discuss the ethical implications of AI use in drug discovery, including data privacy, biases, and the potential impact on the global healthcare landscape.

8. Conclusion This literature review provides a comprehensive overview of AI applications in drug discovery, focusing on molecular docking, target prediction, and drug repurposing. Through trend analysis and a discussion of methodologies, it offers insights into the growing role of