Artificial intelligence (AI) is rapidly reshaping the field of pharmacological research by enabling more efficient, accurate, and cost-effective drug development processes. Through the integration of machine learning, deep learning, and natural language processing, AI tools are enhancing various stages of pharmacological research â from drug discovery and target identification to toxicity prediction, drug repurposing, and personalized medicine. These technologies facilitate the analysis of high-dimensional biological data, predict drugâtarget interactions, and assist in virtual screening of compounds with improved precision. Moreover, AI is proving valuable in post-market surveillance, aiding in the early detection of adverse drug reactions and improving patient safety. Despite its transformative potential, challenges such as data quality, interpretability of models, and ethical considerations remain. This review explores the latest advancements in AI applications within pharmacology, discusses current limitations, and outlines future directions for integrating AI more effectively into drug research and development pipelines.
