Artificial Intelligence and Machine Learning in Drug Discovery: Transformative Applications and Interdisciplinary Integration with Nanotechnology and Robotics

Artificial intelligence (AI) and machine learning (ML) are revolutionizing pharmaceutical research by addressing the persistent challenges of traditional drug discovery, including high costs, lengthy development timelines, and low success rates. This comprehensive review examines the transformative applications of AI/ML across the entire drug discovery pipeline, from target identification to clinical development, while exploring critical interdisciplinary integrations with nanotechnology and robotics. Through systematic analysis of recent advances, we evaluate diverse AI techniques including deep learning, graph neural networks, generative models, and transformers in key areas such as molecular design, lead optimization, ADMET prediction, and clinical trial optimization. The integration of AI with nanotechnology has enabled precise design of nanocarriers for targeted drug delivery, while robotics automation has enhanced high-throughput screening and manufacturing quality control. Despite significant progress, challenges persist in data quality, model interpretability, regulatory frameworks, and ethical considerations. This review synthesizes current applications, identifies critical gaps, and proposes future directions for responsible AI implementation, emphasizing the potential for AI-driven approaches to create safer, more effective, and accessible medicines while maintaining ethical standards and regulatory compliance