Predictive Modeling of Drug Stability Using Artificial Intelligence

Abstract

Drug stability is a fundamental determinant of pharmaceutical quality, efficacy, and patient safety throughout a product's shelf life. Conventional stability assessment relies on long-term experimental studies that require substantial time, laboratory resources, and financial investment. Recent advances in artificial intelligence present opportunities to accelerate stability prediction while reducing dependence on exhaustive experimental testing. This study proposes an explainable multimodal machine learning framework that integrates physicochemical drug properties, storage conditions, and packaging characteristics for predictive stability modeling. The proposed approach employs an ensemble learning strategy combined with feature attribution techniques to improve prediction accuracy and interpretability. A comprehensive experimental protocol is designed using publicly available pharmaceutical descriptors and simulated environmental stability profiles following ICH stability principles. The framework is evaluated using multiple regression performance metrics and compared with conventional machine learning algorithms. The findings demonstrate the potential of explainable AI to support early formulation development, shelf-life estimation, and regulatory decision-making in modern pharmaceutical manufacturing.