Application of Artificial Intelligence in Stability Prediction and Shelf-Life Estimation of Pharmaceutical Formulations

Stability testing determines whether a pharmaceutical product keeps its identity, strength, quality and purity over its labeled shelf life, but conventional studies are slow, expensive and material-intensive, and they offer limited insight into why a formulation degrades. Artificial intelligence (AI), and machine learning (ML) in particular, is increasingly used to predict degradation pathways, physical stability, and time to specification failure from formulation, process, structural and early stability data. This review summarizes the scientific and regulatory background of stability assessment; the main AI methods applied (tree ensembles, kernel methods, Gaussian processes, deep and graph neural networks, and hybrid mechanistic-ML models); data requirements and feature engineering; applications to small molecules, amorphous solid dispersions, biologics, nucleic acids and drug-delivery systems; and an end-to-end workflow for AI-assisted shelf-life estimation. We discuss validation, uncertainty quantification, explainability and regulatory expectations, and outline the challenges that must be solved before AI-derived predictions can support labeled shelf-life claims. Current evidence suggests that AI is best positioned today as a decision-support tool for formulation screening, study design and risk assessment, complementing rather than replacing real-time stability data.