Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations

Functional excipients are increasingly recognized as active components in drug formulations because they can influence drug stability, solubility, release behavior, delivery efficiency, biological barrier penetration, transporter activity, and metabolic clearance. Machine learning is becoming useful in formulation research because it can connect scattered formulation data to excipient selection, property prediction, and experimental decision-making. In particular, machine learning models can help extract useful patterns from fragmented formulation data, prioritize candidate excipients, and guide formulation decisions before extensive experimental screening. This review summarizes recent advances in machine-learning-driven optimization of functional excipients and their biointeractions in drug formulations. We first discuss the methodological foundations of this field, including data acquisition, feature engineering, model architecture selection, optimization, and evaluation strategies. Representative application scenarios are then reviewed, including the identification of functional excipients related to drug efflux inhibition, formulation stability enhancement, biological barrier penetration, and metabolic clearance reduction. This review also discusses practical barriers that still limit this field related to data quality, representation design, experimental feedback, computational cost, and deployability. These issues need to be addressed before artificial intelligence (AI)-assisted formulation systems can become reliable tools for routine pharmaceutical development.