Data-Driven Formulation Based on Integrated Symbolic Regression of Hydrogel Swelling and Molecular Interactions

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Hydrogels offer promising solutions across various fields. However, understanding complex behaviors like swelling and ligand interaction requires multiperspective data, from functional groups to molecular dynamics. Single-perspective analyses often fall short, especially when ligand-induced selective adsorption occurs. This study presents a data-driven approach integrating TD-NMR, 1 H– 15 N HSQC, 1 H– 13 C HNCO with isotope-labeled peptides, RDKit descriptors, and DSC data. Using symbolic regression, we derived highly accurate, interpretable equations (e.g., swelling ratio accuracy = 1.0 on test set). This methodology reveals fundamental hydrogel mechanisms and provides a framework for rational design.