AI-Guided Inverse Design of Hydrocolloid-Based Food Systems: From Molecular Descriptors to Process–Structure–Function Validation

Artificial intelligence (AI) is increasingly being explored for hydrocolloid-based food formulation, yet many applications remain limited by fragmented datasets, weak mechanistic encoding, forward-prediction bias, and limited validation in real food matrices. This review critically examines AI-guided inverse design as a target-to-input strategy that links desired food functionality with hydrocolloid descriptors, processing conditions, structure formation, and experimental validation. Key molecular descriptors, including molecular weight, charge density, degree of modification, hydrophobicity, chain flexibility, and source variability, are evaluated as design inputs rather than simple ingredient labels. These descriptors are connected with interaction mechanisms such as hydrogen bonding, electrostatic complexation, hydrophobic association, ionic crosslinking, thermal gelation, coacervation, and interfacial structuring. The review further compares how processing variables, rheology, microstructure, water mobility, and phase behavior influence outputs such as texture, stability, water retention, printability, encapsulation, and shelf life. Machine-learning approaches are discussed not only for prediction but also for inverse optimization through surrogate modeling, Bayesian optimization, generative and multi-objective search, explainable AI, and closed-loop validation. Current limitations, including overfitting, dataset bias, weak external validation, uncertainty, and poor reporting of failed formulations, are critically assessed. Overall, this review proposes a process-structure-function framework for developing transferable, interpretable, and experimentally validated AI models for hydrocolloid-based food systems.