Multiparametric analysis of functional group–performance relationships in tannic acid-based hydrogels from a pharmaceutical technology standpoint

Tannic acid (TA), a naturally derived polyphenolic compound, offers versatile chemical functionality for the design of advanced hydrogel systems in pharmaceutical applications. However, the relationship between specific functional group architectures and the resultant physicochemical or performance characteristics of TA-based hydrogels remains largely underexplored. In this study, we present an integrative and data-driven framework to elucidate the structure–function relationships in tannic acid–based hydrogels by combining systematic literature screening with artificial intelligence (AI)-based multiparametric modeling. A curated dataset was developed from 29 peer-reviewed publications, including polymer origin, functional groups, characterization techniques, and performance parameters such as swelling capacity, water retention, degradation, mechanical strength, and porosity. Using a suite of AI tools—including artificial neural networks (ANN), decision trees, random forest, and CN2 rule induction—we mapped the presence of key chemical groups (e.g., –COOH, –NH 2 , –OH, –CONH 2 ) to critical formulation attributes. Our findings demonstrate that ionic and polar functional groups, particularly carboxyl and amino moieties, exhibit high predictive relevance for enhanced hydrogel performance, while hydroxyl and ether groups showed minimal contribution in critical domains such as degradation, self-healing, and porosity. Advanced visualizations and rule-based decision models revealed clear trends in the selection and utility of characterization techniques depending on the underlying chemical structure. The use of AI not only enabled a predictive understanding of structure–function correlations but also offered interpretable and generalizable formulation rules for next-generation hydrogel design. This study introduces a novel paradigm for pharmaceutical formulation science, where AI-guided analysis of chemical structure datasets can drive the rational development of functional biomaterials. The approach may significantly reduce empirical trial-and-error, providing a strategic roadmap for tailoring hydrogel systems for drug delivery, tissue engineering, and biomedical implants.