Role of Artificial Intelligence in Drug Discovery Review

Abstract

Artificial intelligence (AI) represents a major paradigm shift in drug discovery, combining computing power, large datasets, and complex algorithms to accelerate new therapeutics. Traditional discovery is long, expensive, and complicated, often taking over a decade and billions of dollars per drug. This review covers AI applications across the pipeline target identification, virtual screening, drug design, lead optimization, ADMET prediction, drug repurposing, and clinical trial optimization along with ML, DL, and NLP approaches that now screen compound libraries in seconds, model drug-target interactions in nanoseconds, and generate new molecules in minutes. AI saves time and money and improves accuracy, but challenges remain around data quality, computation, ethics, and regulation. Emerging areas such as generative AI, digital twins, and precision medicine are also discussed. AI's potential is undeniable, but realizing it requires high-quality data, rigorous validation, and continued human oversight.