Revolutionizing Drug Formulation: Harnessing Artificial Intelligence and Machine Learning for Enhanced Stability, Formulation Optimization, and Accelerated Development

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of pharmaceutical drug formulation has sparked a paradigm shift in the way drug stability is predicted, formulations are optimized, and drug development is expedited. This review delves into the transformative impact of AI and ML techniques on pharmaceutical research and development. It highlights how predictive models driven by AI algorithms are effectively simulating drug degradation pathways and stability profiles, enabling scientists to make informed decisions during formulation design. Moreover, the utilization of ML algorithms to analyze vast datasets has led to the discovery of optimal formulations by identifying critical relationships between formulation variables, excipients, and drug properties. This approach not only reduces experimentation time and costs but also enhances the likelihood of developing robust and effective drug products. Furthermore, AI-powered drug development platforms are shortening the timeline for candidate selection, preclinical evaluations, and clinical trials, thereby accelerating the entire drug development process. This article explores the evolving landscape of AI and ML in drug formulation, discusses challenges, and anticipates future prospects in this transformative field.