DOMINATION OF AI AND MACHINE LEARNING IN PHARMACEUTICAL BIOTECHNOLOGY AND PHARMACOGENOMICS

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) is significantly transforming the landscape of pharmaceutical biotechnology and pharmacogenomics. These technologies are redefining how drugs are discovered, tested, and personalized for individual patients based on their genetic profiles. AI algorithms are now being used to identify potential drug candidates, predict molecular interactions, optimize clinical trials, and tailor therapies to enhance patient outcomes. In the realm of pharmacogenomics, ML models facilitate the interpretation of complex genomic data, enabling clinicians to anticipate drug responses and minimize adverse effects. While the benefits are substantial—ranging from faster innovation cycles to more precise medical interventions—the integration of AI also presents several challenges, including data quality issues, interpretability: of models, regulatory ambiguity, and ethical concerns around privacy and bias. This paper explores the current applications, tools, benefits, limitations, and future directions of AI and ML in pharmaceutical biotechnology and genomic medicine, emphasizing the potential of these technologies to drive a new era of personalized and predictive healthcare.