arXiv Machine Learning By Michael M. Craig, Riley J. Hickman, Yingshan Ma, R\'emi Pich\'e-Taillefer, Christine Allen, Pauric Bannigan

Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory

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The paper introduces Andromeda 2, an agentic system that uses structured in‑house experimental evidence to design and run successive batches of self‑emulsifying drug delivery systems (SEDDS). In a miniaturized automated lab, Andromeda 2 outperformed its predecessor Andromeda 1 and a traditional design‑of‑experiments campaign in developing paclitaxel formulations, achieving a 50 % hit rate versus 17 % and 2 % respectively, and identifying 12 formulations meeting all target product profile objectives. The system’s use of evidence‑grounded reasoning increased mean AUC by 34 % and produced a formulation with a 19 % w/w paclitaxel loading, roughly 3.3‑fold higher than a published benchmark.

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