Artificial Intelligence in Drug Discovery: A New Era of Pharmaceutical Innovation

Drug discovery is a complex, time-consuming, and costly process that traditionally requires over a decade and billions of dollars to bring a single new molecule to market. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool capable of accelerating and optimizing each stage of drug discovery—from target identification to clinical trials. By integrating techniques such as Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Neural Networks (NNs), AI can analyze vast biomedical datasets, identify hidden patterns, and predict drug-target interactions with remarkable accuracy. This technological shift not only reduces research costs but also improves success rates in drug development. This review aims to comprehensively discuss the role of AI in modern pharmaceutical research, highlighting its applications, case studies, challenges, and future prospects. Moreover, the article emphasizes how AI-driven models are reshaping the landscape of personalized medicine and drug repurposing, thereby ushering in a new era of pharmaceutical innovation.