Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects

Abstract

Introduction: The use of Artificial intelligence in drug discovery is changing the field of Medicine across the world today positively. In this review, the role of AI in each focus area for the improvement of the drug development process, and its relevance in translational medicine is discussed. Materials and Method: A systematic review was conducted by searching databases such as PubMed and Scopus, employing key terms like "AI" "drug discovery" "machine learning" "clinical trials" and "translational medicine." Inclusion criteria focused on peer-reviewed studies published between 2014 and 2024 that specifically addressed the role of AI in drug development. Data extraction involved categorizing findings based on different phases of drug discovery. Results: The findings reveal that the use of AI lowers costs, shortens the time required for drug development, and enhances the predictive capability. AI technologies play an essential role in molecular modeling, drug design and screening, and the efficient design of clinical trials. However, some of the issues that remain include the quality of available data, issues of interpretability of the models, and the more critical issue of ethical considerations that need collective efforts on the development of associate regulatory policies. Conclusion: AI holds immense potential to dramatically change and transform the process of drug discovery and translational medicine while promoting accurate prevention and cures. However, it is also important to understand how to work with existing problems to make the best use of AI in healthcare. The roles of AI technologies are likely to grow in the development of the medical future, provide patients with better results, and stimulate the innovations in the field of the drug creation.