ARTIFICIAL INTELLIGENCE IN DRUG DEVELOPMENT: CURRENT APPLICATIONS, CHALLENGES AND FUTURE PERSPECTIVES

Drug development is a complex, time-consuming, and costly process, with average timelines exceeding a decade and success rates remaining low. Artificial intelligence (AI) has emerged as a transformative tool with the potential to accelerate and optimize every stage of the drug development pipeline. By leveraging vast biomedical datasets, advanced algorithms, and computational power, AI can support target identification, predict molecular properties, design novel compounds, and optimize clinical trial processes. Recent advances in machine learning, deep learning, and natural language processing have demonstrated the ability to uncover patterns and relationships beyond the reach of conventional approaches. AI-driven platforms have already contributed to the discovery of promising therapeutic candidates and facilitated more efficient decision-making. Despite significant progress, challenges remain in data quality, model interpretability, regulatory acceptance, and ethical considerations. This review provides a comprehensive overview of AI methodologies applied to drug discovery and development, highlights notable case studies from academia and industry, and critically examines both the opportunities and limitations of these technologies. We also explore future directions, including integration with complementary technologies, collaborative data sharing models, and evolving regulatory frameworks. By evaluating the current state and potential of AI in pharmaceutical research, this article aims to provide scientists, clinicians, and policymakers with a clear understanding of how AI can be harnessed to transform the drug development landscape.