Artificial Intelligence and Machine Learning Across the Pharmaceutical Lifecycle: Applications, Translational Challenges, and Future Directions

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies that are transforming pharmaceutical sciences and healthcare by improving the efficiency, accuracy, and speed of research and development. Conventional pharmaceutical development often involves time-consuming experiments, repeated trials, and substantial financial resources. AI and ML offer promising approaches to overcome these limitations through data analysis, predictive modelling, and process optimization. This review discusses the major AI approaches, including ML, Deep Learning (DL), Generative AI, and Explainable AI, along with their applications in pharmaceutical research and healthcare. Key applications include drug target identification, molecular design, synthesis route prediction, virtual screening, lead optimization, formulation development, advanced drug delivery systems, continuous manufacturing, digital twin technology, predictive maintenance, and supply chain management. AI also supports medical imaging, disease diagnosis, personalized medicine, and healthcare data management. Despite these advances, challenges related to data quality, model validation, regulatory compliance, implementation costs, data privacy, and the availability of skilled professionals remain. Future developments may strengthen the integration of AI into pharmaceutical research, manufacturing, and clinical practice. Overall, AI and ML have considerable potential to support more efficient pharmaceutical development, improve product quality, and contribute to better patient outcomes.