Abstract
Traditional drug discovery relies on manual research, high-throughput screening, and structure-based design, requiring 13–15 years and exceeding $2.5 billion investment with less than 10% FDA approval rates. This paper presents a comprehensive analysis of how Artificial Intelligence (AI) revolutionizes pharmaceutical development by accelerating drug discovery, predicting molecular properties, and optimizing clinical trials. We examine AI applications across six critical domains: target identification, virtual screening, quantitative structure-activity relationship (QSAR) modeling, ADMET prediction, drug repurposing, and clinical trial optimization. Through a systematic implementation framework integrating machine learning, deep learning, and graph neural networks, AI reduces development timelines by up to 40%, decreases failure rates, and enables precision medicine through personalized treatment strategies. This work synthesizes current methodologies, demonstrates practical applications using real-world data models, and addresses critical challenges including data quality, regulatory compliance, and model interpretability. Our findings confirm that AI-driven approaches significantly improve hit enrichment rates and therapeutic efficacy while reducing computational costs, establishing AI as an indispensable tool for modern pharmaceutical innovation.