Generative Artificial Intelligence in Pharmaceutical Formulation Development

Abstract

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in pharmaceutical sciences, particularly in formulation development and personalized medicine. Traditional pharmaceutical formulation relies heavily on trial-and-error approaches, consuming substantial time, labor, and financial resources. Recent advances in machine learning, deep learning, large language models (LLMs), and generative neural networks have enabled predictive and data-driven formulation strategies. GenAI technologies can optimize excipient selection, predict physicochemical properties, enhance stability studies, simulate dissolution profiles, and accelerate dosage form design. Applications now extend to nanotechnology-based drug delivery systems, 3D printed medicines, personalized dosage forms, and intelligent process optimization. Despite significant progress, challenges such as limited datasets, regulatory uncertainty, explainability issues, ethical concerns, and data security remain barriers to widespread implementation. This review summarizes the principles of generative AI, its integration into pharmaceutical formulation development, recent advancements, industrial applications, limitations, regulatory considerations, and future opportunities. The review highlights the growing potential of GenAI to revolutionize pharmaceutical product development by reducing costs, accelerating timelines, and enabling precision medicine approaches.