@article{hoffman2026artificial,
  title = {Artificial Intelligence and Design of Experiments in the  Development of Pharmaceutical Formulations:  A Taxonomy of Approaches},
  author = {Michal HOFFMAN and Marcin MRUGALSKI},
  year = 2026,
  url = {https://ibimapublishing.com/p-articles/47AI/2026/4720926/},
  journal = {Communications of International Proceedings},
  volume = 2026 (12),
  abstract = {Traditional Design of Experiments (DoE) methods in pharmaceutical formulation design, which rely mainly on static linear regression, have limitations when modelling multidimensional and non-linear biological systems. The growing complexity of modern overcoming these limitations using AI methods was highlighted. Furthermore, AI methods used in the design and delivery of pharmaceutical formulations methods necessitates the search for highly flexible alternatives for multi-criteria optimisation of substances. Despite the advanced capabilities of Artificial Intelligence (AI), its widespread adoption in research and development stages is still heavily inhibited by concerns regarding a lack of transparency in the pharmaceutical formulations design process and shortages in the availability of representative clinical data. Furthermore, a very clear gap can be observed in the current literature, manifested by the lack of a coherent, universal conceptual framework linking complex algorithms with conventional DoE schemes. To address this issue, the article introduces a new taxonomy categorising AI methodology in the field of DoE into five key groups: Predictive Machine Learning (PML), Deep Learning (DL), Adaptive DoE (ADoE), Global Optimisation (GO), and Generative and Support AI (G&S AI). Analysis of the applications presented clearly demonstrates that the fusion of adaptive DoE with DL or PML enables the precise development of pharmaceutical formulations.},
  keywords = {Artificial intelligence, designs of experiments, pharmaceutical formulations design, predictive machine learning, deep learning, adaptive DoE, global optimisation, and generative and support AI.},
  note = Article ID: 4720926
}