Abstract

The advent of Generative Artificial Intelligence (AI) has revolutionized various fields, including pharmacology. This paper explores the application of generative AI in the classification of pharmacological data, focusing on its potential to enhance drug discovery, optimize therapeutic strategies, and improve patient outcomes. I present a comprehensive analysis of methodologies, results, and implications of using generative AI in pharmacology. The findings indicate that generative AI can significantly improve classification accuracy and efficiency, paving the way for more personalized medicine. The integration of generative AI into pharmacological classification represents a significant advancement in the field of medical sciences. The methodologies and findings presented in this paper underscore the potential of generative AI to enhance drug discovery processes and improve patient outcomes. As research in this area progresses, addressing ethical considerations and fostering interdisciplinary collaboration will be crucial for realizing the full potential of generative AI in pharmacology.

Keywords: Generative AI, Pharmacology, Drug classification, Machine learning, Medical sciences, Data analysis, Therapeutic strategies, Personalized medicine