This paper outlines how artificial intelligence is reshaping pharmaceutical and biotechnology research by accelerating discovery, optimizing development, and improving decision-making. It describes how AI techniques such as machine learning, natural language processing, and predictive modeling enable scientists to extract insights from complex, high-volume datasets. Use cases include target identification, molecule design, clinical trial optimization, and real-world evidence analysis. The paper also discusses the importance of high-quality, well-structured data and secure platforms to ensure AI outputs are reliable and compliant. By integrating AI into existing scientific workflows, organizations can reduce cycle times, increase success rates, and unlock new therapeutic opportunities.

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