Artificial Intelligence for Adaptive Drug Formulation Optimization

Abstract

Artificial intelligence is increasingly transforming drug formulation development by reducing experimental workload, improving prediction accuracy, and accelerating optimization. This study examines the use of machine learning, deep learning, Bayesian optimization, and adaptive learning for identifying optimal combinations of active pharmaceutical ingredients, excipients, and process parameters. The proposed framework continuously updates its predictions using new experimental data, allowing more efficient and accurate formulation design. Results indicate that the adaptive deep learning framework outperforms conventional statistical and machine learning models, achieving higher predictive accuracy and reducing the number of laboratory trials. Explainable AI techniques further improve transparency by identifying the variables that most strongly influence formulation quality. Although challenges related to data quality, model interpretability, and regulatory validation remain, AIbased adaptive optimization offers significant potential to improve pharmaceutical development, manufacturing efficiency, and product quality.