A Review on Progress and Potential of Machine Learning and AI in Pharmaceutical Development

Review Article

Abstract

The integration of artificial intelligence (AI) in pharmaceutical technology represents a transformative shift in how drugs are discovered, developed, and manufactured. Recent advancements in machine learning algorithms, deep neural networks, and computational power have accelerated drug discovery timelines and enhanced manufacturing efficiency. AI technologies have demonstrated remarkable capabilities in target identification, lead optimization, and prediction of drug-protein interactions. In pharmaceutical manufacturing, AI-driven process analytical technology (PAT) systems optimize production parameters, ensure quality control, and enable real-time monitoring of critical process parameters. The implementation of AI in pharmaceutical analysis has revolutionized quality testing procedures, automated analytical processes, and improved predictive maintenance strategies. Despite these advances, the pharmaceutical industry faces challenges in AI adoption, including data quality concerns, regulatory compliance, and technical implementation barriers. Current regulatory frameworks are evolving to accommodate AI-based systems while maintaining stringent quality and safety standards. Looking ahead, emerging technologies such as quantum computing and federated learning promise to further enhance AI capabilities in drug development. The convergence of AI with other cutting-edge technologies positions the pharmaceutical industry for unprecedented innovation in therapeutic development and manufacturing excellence. The aim of this review is to study about the current state, applications, challenges, and future trajectory of AI in pharmaceutical technology, emphasizing its role in shaping the future of medicine.