AI Driven Formulation Optimization & Predictive Modeling

Abstract

Artificial Intelligence (AI) is transforming the landscape of formulation development and predictive modeling in pharmaceuticals, chemicals, and material sciences. Traditional trial-and-error approaches are time consuming, resource-intensive, and often yield suboptimal formulations. AI driven methods, including machine learning (ML), deep learning (DL), and hybrid optimization algorithms, offer the potential to predict formulation behavior, enhance product performance, and reduce development time. This review explores recent advancements in AI applications for formulation optimization, predictive modeling, and decision-making processes. The paper also discusses challenges in integrating AI into industrial workflows and future perspectives for intelligent formulation development.

KEYWORDS: Artificial intelligence, formulation optimization, predictive modeling, machine learning, deep learning, pharmaceutical formulations, process optimization.