Interpretable Machine Learning Insights into Adhesion and Modulus of Biomedical HA-Dopamine Hydrogels

Hyaluronic acid-dopamine (HA-Dopa) hydrogels have emerged as promising adhesive biomaterials for biomedical applications. However, the complex dependencies between formulation parameters and hydrogel performance pose challenges for rational material design. In this study, an interpretable machine learning framework was developed to investigate the structure-property relationships of HA-Dopa hydrogels. A dataset comprising 228 data points was collected from 37 peer-reviewed publications, representing heterogeneous experimental conditions across different research groups, and gradient boosting regression models were established to predict adhesion strength and elastic modulus, achieving test R² of 0.99 and 0.94, respectively, with stable performance across cross-validation splits. SHAP analysis revealed that HA molecular weight and dopamine substitution degree are the dominant factors governing adhesion, while mechanical properties exhibit more distributed dependence on multiple formulation parameters. The identified synergistic interactions between key features provide potential guidance for targeted formulation optimization. This work demonstrates the utility of interpretable machine learning in elucidating structure-property relationships and accelerating the development of functional hydrogels for biomedical applications.