Artificial Intelligence in Pharmaceutical Preformulation and Stability: A Paradigm Shift

The integration of artificial intelligence (AI) and machine learning (ML) into pharmaceutical sciences has significantly transformed traditional approaches in preformulation and stability assessment. Historically reliant on empirical and time-intensive methodologies, pharmaceutical development is now benefitting from data-driven models that accelerate formulation design, improve precision, and reduce development time. This review explores critical innovations including solubility prediction using machine learning algorithms, AI-supported polymorph identification, and computational modeling for drug stability forecasting aligned with ICH Q1A(R2) guidelines. Through real-world case studies, we discuss the profound impact of these tools on reducing post-market formulation failures, optimizing bioavailability, and supporting regulatory compliance. Challenges such as data transparency, model validation, and regulatory adaptation are addressed. Ultimately, the convergence of AI with pharmacokinetic modeling is ushering in an era of patient-centric, robust, and efficient drug development.