摘要
Nerve‐on‐a‐chip platforms transform neurological research by providing customized microenvironments, thereby reducing reliance on animal testing. However, optimizing these biochips remains challenging due to complex interdependencies among critical fabrication parameters. Here, a machine learning (ML)–guided workflow is developed combining cell viability assays, data augmentation, ensemble modeling, and model interpretation to predict the viability of isolated cortical rat neural stem/progenitor cells (rat NSPCs) and extract data‐driven insights. Initially, rat NSPCs are cultured on 102 distinct hydrogels fabricated with varying parameters, and their attachment conditions and cell viability are evaluated. To mitigate overfitting issue, 10–20 times more virtual data points are generated from characterized NSPC attachment and viability data. A support vector machine classifier is trained on attachment data to define a feasible parameter space where NSPCs consistently adhere to biointerfaces. Subsequently, a prediction model is constructed to predict rat NSPC viability. Model interpretation reveals that biointerface stiffness is the primary factor for rat NSPC attachment and viability, with peptide loading also contributing. Leveraging these insights, the stiffness of a nerve‐on‐a‐chip platform is optimized by adjusting the bilayer number of a polyelectrolyte multilayer, successfully enhancing neurite outgrowth. This versatile ML workflow can be extended to other bioengineering systems. Compared to labor‐intensive trial‐and‐error experimentation, a machine learning (ML)‐guided workflow, incorporating cell viability assays, data augmentation, ensemble modeling, and model interpretation, is developed to accelerate nerve‐on‐a‐chip optimization and uncover data‐driven design principles. This integrated workflow successfully identifies biointerface stiffness as the primary parameter regulating rat neural stem/progenitor cell (rat NSPC) behaviors and facilitates the design of the nerve‐on‐a‐chip platform.