Artificial Intelligence and the Generative Science of Food Formulation

Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Now, artificial intelligence (AI) offers the opportunity to accelerate this process. Yet despite rapid advances across food science, most AI applications remain isolated prediction and optimization tasks rather than parts of a broader scientific approach. Here we integrate these emerging technologies into a unified framework—the generative science of food formulation—in which digital food representations enable AI to predict, discover, generate, organize, simulate, and optimize. We illustrate this approach through sustainability and nutrition, where generative AI transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances have the potential to transform food formulation from an empirical discipline into a generative science.

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Publication Details

Journal
Annual Review of Food Science and Technology
Published
2026-11-04
DOI
https://doi.org/10.1146/annurev-food-020326-031633
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00

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article

Artificial Intelligence and the Generative Science of Food Formulation

Vahidullah Taç, Ellen Kuhl A
4 links
Annual Review of Food Science and Technology
Machine Learning in Materials Science
article

Artificial Intelligence and the Generative Science of Food Formulation

Vahidullah Taç, Ellen Kuhl A
article en

Abstract

Food formulation requires balancing taste, nutrition, sustainability, and cost. Traditionally, new foods have emerged through empirical experimentation, expert intuition, and iterative refinement. Now, artificial intelligence (AI) offers the opportunity to accelerate this process. Yet despite rapid advances across food science, most AI applications remain isolated prediction and optimization tasks rather than parts of a broader scientific approach. Here we integrate these emerging technologies into a unified framework—the generative science of food formulation—in which digital food representations enable AI to predict, discover, generate, organize, simulate, and optimize. We illustrate this approach through sustainability and nutrition, where generative AI transforms environmental and nutritional metrics from post hoc evaluation criteria into explicit design objectives. Finally, we identify the data, models, benchmarks, and automation that will establish computational food design as a rigorous scientific discipline. Together, these advances have the potential to transform food formulation from an empirical discipline into a generative science.

Annual Review of Food Science and Technology
Stanford University (US)
National Science Foundation, Stanford Bio-X, Division of Civil, Mechanical and Manufacturing Innovation
Zero hunger
Openalex Percentile: Top 66%
Machine Learning in Materials Science
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