AI convergence in drug development and recent applications: A review

The combination of artificial intelligence (AI) and medication research has resulted in a dramatic shift in the pharmaceutical sector. AI-driven technologies, including as machine learning (ML), deep learning (DL), and natural language processing (NLP), are rapidly being used at all stages of drug discovery, from target identification to clinical trials. This paper delves into current advances in AI applications throughout the drug development pipeline, demonstrating how AI models are improving predictive accuracy, optimizing compound screening, and expediting lead identification. We investigate the use of AI in repurposing current medications, discovering biomarkers for personalized medicine, and enhancing clinical trial design by predicting patient responses and optimizing dosing regimes. Furthermore, we describe how multi-omics data, AI-driven simulation models, and automated high-throughput screening technologies are accelerating the usually lengthy and expensive drug discovery process. Despite AI's promise, obstacles persist in data quality, model interpretability, and regulatory hurdles. The review concludes by outlining future directions for AI in drug development, emphasizing the importance of interdisciplinary collaboration and the potential for AI to revolutionize the way drugs are discovered and brought to market, offering new hope for precision medicine and the treatment of complex diseases.