Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Abstract Artificial intelligence (AI) is being integrated into nearly every aspect of modern life, and machine learning (ML)–a subfield of AI–has the potential to accelerate the development of nanomedicines. Here, a machine learning workflow is presented to optimize the microfluidic‐based formulation of nanomedicines. A database of ≈200 unique nanomedicine formulations with over 550 total measurements is curated by producing liposomes, lipid nanoparticles, and poly(lactic‐ co ‐glycolic acid) nanoparticles, either empty or loaded with the model therapeutic agent, curcumin. Nanoparticle production parameters are systematically varied, and the resulting particles are characterized for their diameter, polydispersity index, and encapsulation efficiency. These data are used to train and validate 13 different ML models using open‐source libraries, with the task of returning the most accurate prediction of nanomedicine attributes. The most accurate models, based on random forest regression, are implemented to yield particles with user‐specified attributes. Finally, the proposed ML workflow, MicrofluidicML, is compared against generative large language models–OpenAI ChatGPT, Google's Gemini, and DeepSeek. MicrofluidicML provides a workflow where the researcher has complete governance and control of the input data with a relatively low computational overhead, and represents a step toward implementing a computationally lightweight ML framework to accelerate nanomedicine development.