The review article provides a comprehensive overview of the integration of artificial intelligence (AI) across the entire drug development pipeline, which is traditionally costly and time-consuming. It details the application of various AI technologies, including large language models (LLMs) and generative AI, in stages such as target identification, drug discovery, preclinical and clinical studies, and post-market surveillance. The text highlights how AI is being used for complex tasks like virtual screening, de novo molecular design, and predicting ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, aiming to increase efficiency and success rates. Furthermore, the source examines the current challenges facing AI-augmented drug development, such as data scarcity and interpretability issues, before discussing future research directions in this rapidly evolving field.
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