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However, bioformulation of complex proteins is a difficult engineering challenge; formulations must be tailored to individual therapies, necessitating time\u2010 and material\u2010intensive campaigns to select combinations of excipients to simultaneously optimize various design criteria. These additives complicate formulation design with unintuitive and non\u2010linear relationships, creating a vast multidimensional design space that is intrinsically difficult to optimize using traditional techniques. To address this challenge, we investigated a high\u2010throughput discovery pipeline using machine learning to model and predict the effects of GRAS excipients on formulation behavior of a model antibody. This was supported by automation\u2010assisted \u201con\u2010demand\u201d formulation to produce dozens of uniquely formulated antibody solutions for downstream evaluation and biophysical characterization. This pipeline was then integrated into an iterative closed\u2010loop cycle of automated Design\u2010Build\u2010Test\u2010Learn (DBTL), where new rounds of experiments are designed by the model. The process yielded both improved formulations and accurate predictive models of formulation behavior across multiple target objectives (melting temperature, diffusivity, and high\u2010concentration viscosity). This validates the utility of this technique to both map the underlying property\u2010function landscape and effectively guide formulation development while balancing multiple competing design requirements.<\/jats:p>","DOI":"10.1002\/advs.76551","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:41:17Z","timestamp":1783946477000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automation and Active Learning for the Multi\u2010Objective Optimization of Antibody Formulations"],"prefix":"10.1002","volume":"13","author":[{"given":"D. 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