AI-Assisted Formulation Development: From Trial-and-Error to Predictive Drug Delivery

Abstract

Conventional development depends on experiment design, mechanistic comprehension, and repeated experimentation, but as formulation complexity rises, it becomes more challenging to explore the search space. To forecast material properties, drug–excipient compatibility, dissolution, stability, particle attributes, drug loading, release kinetics, pharmacokinetics, and process behavior, artificial intelligence (AI), machine learning (ML), Bayesian optimization, deep learning, generative models, and hybrid mechanistic–data-driven systems are being introduced. The stronger assertion that AI has typically replaced conventional development or consistently produced transferable superiority is not yet supported by the literature, but it does support a significant change from unstructured trial-and-error toward model-guided experimentation. Narrative reviews, retrospective models, tiny private datasets, and application-specific demonstrations continue to make up most of the published evidence. In order to assess whether AI-assisted formulation becomes predictive rather than merely descriptive, this review looks at the computational techniques, formulation domains, data needs, validation standards, regulatory consequences, and translational impediments. A workable implementation architecture is put out in which AI enhances rather than replaces quality by design (QbD), design of experiments (DoE), process analytical technology (PAT), mechanistic modeling, and human formulation knowledge.