Artificial intelligence for food innovation

Global food systems must deliver nutritious, sustainable foods while sharply reducing environmental impact. Yet, food innovation remains slow, empirical and fragmented. Artificial intelligence (AI) offers a transformative path to link molecular composition to functional performance, connect chemical structure to sensory outcomes and accelerate cross-disciplinary innovation across the production pipeline. While it is broadly applicable to food systems, we focus on sustainable proteins—plant-based, fermentation-derived and cultivated—as a high-impact test bed for AI-driven closed-loop design. We review the applications, opportunities and challenges of AI for food as an emerging discipline that integrates ingredient design, formulation development, fermentation and production, texture analysis, sensory science, manufacturing and recipe generation. We identify four priorities: advancing scientific machine learning with embedded domain priors, treating food as a programmable biomaterial, building self-driving laboratories for automated discovery and developing deep reasoning models that integrate nutrition and sustainability. Integrating AI responsibly into the food innovation cycle can accelerate the transition to sustainable food systems and establish a predictive, design-driven science of food for human and planetary health.

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Lappé, F. M. Diet for a Small Planet (Ballantine Books, 1971).

Clark, M. A. et al. Global food system emissions could preclude achieving the 1.5° and 2°C climate change targets. Science 370, 705–708 (2020).

Article ADS CAS PubMed Google Scholar

Xu, X. et al. Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nat. Food 2, 724–732 (2021). This study shows that animal-based foods produce roughly twice the greenhouse gases of plant-based foods. It underscores the environmental benefits of plant and microbial proteins central to sustainable food design.

Article CAS PubMed Google Scholar

Keesing, F. Diet for a small footprint. Proc. Natl Acad. Sci. USA 119, e2204241119 (2022).

Article CAS PubMed PubMed Central Google Scholar

Friedrich, B. Transforming a 12,000-year-old technology. Nat. Food 3, 807–808 (2022).

Barabasi, A. L. et al. The unmapped chemical complexity of our diet. Nat. Food 1, 33–37 (2020). This perspective reveals the vast and largely unexplored chemical diversity in everyday foods. It highlights the need for computational and AI-driven mapping of food molecules to understand nutrition, flavour and health interactions.

Zeni, C. et al. A generative model for inorganic materials design. Nature 639, 624–632 (2025).

Article ADS CAS PubMed PubMed Central Google Scholar

Al-Sarayreh, M. et al. Inverse design and AI/deep generative networks in food design: a comprehensive review. Trends Food Sci. Technol. 138, 215–228 (2023).

Datta, A. et al. Computer-aided food engineering. Nat. Food 3, 894–904 (2022). This review outlines how computation, modelling and automation can transform food engineering. It establishes a framework for integrating AI, digital twins and process optimization into food design and manufacturing.

King, A. Four ways to power-up AI for drug discovery. Nature https://doi.org/10.1038/d41586-025-00602-5 (2025).

Kuhl, E. AI for food: accelerating and democratizing discovery and innovation. npj Sci. Food 9, 82 (2025).

Article PubMed PubMed Central Google Scholar

Turing, A. M. Computing machinery and intelligence. Mind 59, 433–460 (1950).

Article MathSciNet Google Scholar

Rumelhart, D. E. et al. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).

Krizhevsky, A. et al. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

Goodfellow, I. et al. Generative adversarial networks. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014). This work presents the GAN framework for generating realistic synthetic data. It underpins AI applications in food imaging, formulation design and sensory data augmentation.

Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017). This landmark paper introduces the transformer architecture of today’s large language models. It enables multimodal AI systems for recipe generation, ingredient substitution and scientific discovery.

International Human Genome Sequencing Consortium Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

Gilmer, J. et al. Neural message passing for quantum chemistry. Proc. Int. Conf. Mach. Learn. 34, 1263–1272 (2017). This paper introduces message-passing neural networks for predicting molecular properties from graph structures. It lays the foundation for AI models in food chemistry and ingredient design.

Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

Gordon, E. B. et al. Biomaterials in cellular agriculture and plant-based foods for the future. Nat. Rev. Mater. 10, 500–518 (2025).

Schreurs, M. et al. Predicting and improving complex beer flavor through machine learning. Nat. Commun. 15, 2368 (2024).

Lee, B.-K. et al. A principal odor map unifies diverse tasks in olfactory perception. Science 381, 999–1006 (2023). This work creates a continuous map linking molecular structure to human odour perception. It enables AI models to predict and generate new flavour and aroma profiles for food design.

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). This breakthrough model achieves near-experimental accuracy in protein structure prediction. It inspires the design of functional food proteins and enzymes for alternative protein production.

Butler, K. T. et al. Machine learning for molecular and materials science. Nature 559, 547–555 (2018). This review outlines how machine learning accelerates discovery in chemistry and materials science. It provides key methodologies directly transferable to AI-based formulation and texture engineering in food.

Graham, A. E. & Ledesma-Amaro, R. The microbial food revolution. Nat. Commun. 14, 2231 (2023).

Lurie-Luke, E. Alternative protein sources: science-powered startups to fuel food innovation. Nat. Commun. 15, 4425 (2024).

Kyriakopoulou, K. et al. in Sustainable Meat Production and Processing (ed. Galanakis, C. M.) 103–126 (Academic Press, 2019).

Fasolin, L. H. et al. Emergent food proteins—towards sustainability, health and innovation. Foods 10, 600 (2021).

Day, L. Proteins from land plants—potential resources for human nutrition and food security. Trends Food Sci. Technol. 32, 25–42 (2013).

Liu, M.-Q. et al. Digging natural emulsifiers based on machine learning and exploration their performance for stabilizing dairy products. Preprint at SSRN https://ssrn.com/abstract=4994781 (2025).

Siejak, P. et al. The prediction of pectin viscosity using machine learning based on physical characteristics—case study: aglupectin HS-MR. Sustainability 16, 5877 (2024).

Article ADS CAS Google Scholar

Lie-Piang, A. et al. Quantifying techno-functional properties of ingredients from multiple crops using machine learning. Curr. Res. Food Sci. 7, 100601 (2023).

Yilmaz, M. T. et al. Explainable AI-driven evaluation of plant protein rheology using tree-based and Gaussian process machine learning models. Ain Shams Eng. J. 16, 103565 (2025).

Kraessig, P. M. et al. Sensory-biased autoencoder enables prediction of texture perception from food rheology. Food Res. Int. 205, 116007 (2025).

Meng, Y. et al. Advancing salt reduction technologies: AI-assisted structural design of starch-based emulsion gel systems for next-generation low sodium food formulations. Trends Food Sci. Technol. 264, 105234 (2025).

NotCo. Latent space method of generating food formulas. US patent 10,915,818 (2021).

NotCo. Neural network method of generating food formulas. US patent 10,957,424 (2021).

NotCo. Systems and methods to mimic target food items using artificial intelligence. US patent 11,164,478 (2021).

Martin, J. et al. Recipe1M+: a dataset for learning cross-modal embeddings for cooking recipes and food images. IEEE Trans. Pattern Anal. Mach. Intell. 43 187–203 (2021).

Park, D. et al. FlavorGraph: a large-scale food-chemical graph for generating food representations and recommending food pairings. Sci. Rep. 11, 931 (2021).

Garg, N. et al. FlavorDB: a database of flavor molecules. Nucl. Acids Res. 46, D1210–D1216 (2018).

Ahn, Y.-Y. et al. Flavor network and the principles of food pairing. Sci. Rep. 1, 196 (2011).

Tac, V., Gardner, C. & Kuhl, E. Generative artificial intelligence creates delicious, sustainable, and nutritious burgers. Preprint at https://arxiv.org/abs/2602.03092 (2026). This study uses generative AI to create burgers that outperformed the classic Big Mac in a blinded restaurant survey.

van den Bedem, S. D. et al. Open-source benchmarking of plant-based and animal meats. Foods 15, 2112 (2026).

Eastham, J. L. & Leman, A. R. Precision fermentation for food proteins: ingredient innovations, bioprocess considerations, and outlook—a mini-review. Curr. Opin. Food Sci. 58, 101194 (2024).

Feng, X. et al. Enhanced lipid production by Chlorella pyrenoidosa through magnetic field pretreatment of wastewater and treatment of microalgae-wastewater culture solution: magnetic field treatment modes and conditions. Bioresour. Technol. 206, 123102 (2020).

Sharma, D. et al. Response surface methodology and artificial neural network modelling for enhancing maturity parameters during vermicomposting of floral waste. Bioresour. Technol. 324, 124672 (2021).

Liao, X. et al. Artificial Intelligence: a solution to involution of design–build–test–learn cycle. Curr. Opin. Biotechnol. 75, 102712 (2022).

Liu, L. et al. Modeling and optimization of microbial hyaluronic acid production by Streptococcus zooepidemicus using radial basis function neural network coupling quantum-behaved particle swarm optimization algorithm. Biotechnol. Prog. 25, 1819–1825 (2009).

Jia, R. et al. Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors. Bioresour. Technol. 363, 127908 (2022).

Cheng, Y. et al. Artificial intelligence technologies in bioprocess: opportunities and challenges. Bioresour. Technol. 369, 128451 (2023).

Treloar, N. J. et al. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput. Biol. 16, e1007783 (2020).

Singhal, A. et al. Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis. Bioresour. Technol. 265, 214–223 (2018).

Austerjost, J. et al. A machine vision approach for bioreactor foam sensing. SLAS Technol. 26, 408–414 (2021).

Butean, A. et al. A review of artificial intelligence applications for biorefineries and bioprocessing: from data-driven processes to optimization strategies and real-time control. Processes 13, 2544 (2025).

Alejo, L. et al. Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques. Environ. Sci. Pollut. Res. Int. 25, 21149–21163 (2018).

Akinade, O. O. & Oyedele, L. O. Integrating construction supply chains within a circular economy: an ANFIS-based waste analytics system (A-WAS). J. Clean. Prod. 229, 863–873 (2019).

Peng, J. et al. Time-dependent fermentation control strategies for enhancing synthesis of marine bacteriocin 1701 using artificial neural network and genetic algorithm. Bioresour. Technol. 138, 345–352 (2013).

Sharma, D. & Singh, K. AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations. World J. Microbiol. Biotechnol. 41, 278 (2025).

Holzinger, A. et al. AI for life: trends in artificial intelligence for biotechnology. New Biotechnol. 74, 16–24 (2023).

Ghafarollahi, A. & Buehler, M. J. ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digit. Discov. 3, 1389–1409 (2024).

St. Pierre, S. R. et al. Discovering the mechanics of artificial and real meat. Comput. Methods Appl. Mech. Eng. 415, 116236 (2023).

St. Pierre, S. R. et al. The mechanical and sensory signature of plant-based and animal meat. npj Sci. Food 8, 94 (2024). This study quantitatively links mechanical texture measurements with human sensory perception in plant-based and animal meats. It provides critical benchmark data for AI models to predict texture and mouthfeel from physical properties.

Sanahuja, M. et al. Classification of puffed snacks freshness based on crispiness-related mechanical and acoustical properties. J. Food Eng. 226, 53–64 (2018).

Dahl, J. F. et al. Predicting rheological parameters of food biopolymer mixtures using machine learning. Food Hydrocoll. 160, 110786 (2025).

Zhang, Y. et al. Construction of an intelligent recognition system for the cooking doneness of deep-fried golden pompano (Trachinotus ovatus) based on deep learning. Food Biosci. 68, 106644 (2025).

Saalbrink, J. et al. Quantifying microscopic droplets in colloidal systems through machine learning-based image analysis. Food Hydrocoll. 166, 111301 (2025).

Dunne, R. A. et al. Texture profile analysis and rheology of plant-based and animal meat. Food Res. Int. 205, 115876 (2025).

Munekata, P. E. S. et al. Applications of electronic nose, electronic eye and electronic tongue in quality, safety and shelf life of meat and meat products: a review. Sensors 23, 672 (2023).

Karakaya, D. et al. Electronic nose and its applications: a survey. Int. J. Autom. Comput. 17, 179–209 (2019).

Bengio, Y. et al. Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1798–1828 (2012).

Tom, G. et al. From molecules to mixtures: learning representations of olfactory mixture similarity using inductive biases. Preprint at https://arxiv.org/abs/2501.16271 (2025).

Istif, E. et al. Miniaturized wireless sensor enables real-time monitoring of food spoilage. Nat. Food 4, 427–436 (2023).

Watson, N. J. et al. Intelligent sensors for sustainable food and drink manufacturing. Sustain. Food Syst. 5, 642786 (2021).

Rombach, R. et al. High-resolution image synthesis with latent diffusion models. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 10684–10695 (IEEE, 2022).

Dinali, M. et al. Fibrous structure in plant-based meat: high-moisture extrusion factors and sensory attributes in production and storage. Food Rev. Int. 40, 2940–2968 (2024).

Jiang, Y. et al. Plant-based protein extrusion optimization: comparison between machine learning and conventional experimental design. Curr. Res. Food Sci. 11, 101157 (2025).

Abdurrahman, E. E. M. et al. Digital twin applications in the food industry: a review. Front. Sustain. Food Syst. 9, 1538375 (2025).

Top, J. et al. Cultivating FAIR principles for agri-food data. Comput. Electron. Agric. 196, 106909 (2022).

Bölücü, N. et al. An evaluation of large language models for supplementing a food extrusion dataset. Foods 14, 1355 (2025).

Watson, N. & Rafiq, S. NAPIC Partner Engagement Workshop Report (Univ. Leeds, 2025); https://doi.org/10.48785/100/320

Reducing the Price of Alternative Proteins (Good Food Institute, 2025); https://gfi.org/reducing-the-price-of-alternative-proteins

Mohbat, F. & Mohammed, J. Z. Llava-chef: a multi-modal generative model for food recipes. In Proc. 33rd ACM International Conference on Information and Knowledge Management (ACM, 2024).

Li, P. et al. Cheffusion: multimodal foundation model integrating recipe and food image generation. In Proc. 33rd ACM International Conference on Information and Knowledge Management (ACM, 2024).

Senath, T. et al. Large language models for ingredient substitution in food recipes using supervised fine-tuning and direct preference optimization. Preprint at https://arxiv.org/abs/2412.04922 (2024).

Tac, V. & Kuhl, E. Generative AI for material design: a mechanics perspective from burgers to matter. Comp. Meth. Appl. Mech. Eng. 61, 119171 (2026).

Liu, G. et al. Retrieval augmented recipe generation. In IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (IEEE, 2025).

Thomas, A. T. et al. What can large language models do for sustainable food? Proc. 42nd Int. Conf. Mach. Learn 267, 59377–59433 (PMLR, 2025).

Yang, D. et al. Social skill training with large language models. Preprint at https://arxiv.org/abs/2404.04204 (2024).

Rosas, L. G. et al. The effectiveness of Recipe4Health: a quasi-experimental evaluation. Am. J. Prevent. Med. 68, 377–390 (2025).

Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

Yuen, J. S. K. et al. Aggregating in vitro-grown adipocytes to produce macroscale cell-cultured fat tissue with tunable lipid compositions for food applications. eLife 12, e82120 (2023).

Vervenne, T. et al. Probing mycelium mechanics and taste: the moist and fibrous signature of fungi steak. Acta Biomat. 202, 341–351 (2025).

Rackauckas, C. et al. Universal differential equations for scientific machine learning. Preprint at https://arxiv.org/abs/2001.04385 (2020).

Raissi, M. et al. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019).

Article ADS MathSciNet Google Scholar

Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Rev. Phys. 3, 422–440 (2021).

Linka, K. & Kuhl, E. A new family of constitutive artificial neural networks towards automated model discovery. Comput. Methods Appl. Mech. Eng. 403, 115731 (2023).

Brunton, S. L. et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl Acad. Sci. USA 113, 3932–3937 (2016).

Article ADS MathSciNet CAS PubMed PubMed Central Google Scholar

St. Pierre, S. R. et al. Biaxial testing and sensory texture evaluation of plant-based and animal deli meat. Curr. Res. Food Sci. 10, 101080 (2025).

Ghafarollahi, A. & Buehler, M. J. Sparks: multi-agent artificial intelligence model discovers protein design principles. Preprint at https://arxiv.org/abs/2504.19017 (2025).

Roohani, Y. et al. BioDiscoveryAgent: an AI agent for designing genetic perturbation experiments. Preprint at https://arxiv.org/abs/2405.17631 (2024).

Swanson, K. et al. The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies. Nature 646, 716–723 (2025).

Buehler, M. J. Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning. Mach. Learn. Sci. Technol. 5, 035083 (2024).

Boiko, D. A. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). This paper demonstrates how large language models can autonomously plan, execute and interpret chemical experiments. It marks a major step towards self-driving discovery systems, directly inspiring similar AI-driven pipelines for food formulation and ingredient design.

Peirlinck, M. et al. On automated model discovery and a universal material subroutine for hyperelastic materials. Comput. Methods Appl. Mech. Eng. 418, 116534 (2024).

We thank H. Fattaey for stimulating discussions on the future of food, R. Yow for helping design Fig. 3c and G. Atanasovski for illustrating Figs. 3 and 4.

D.L.K. discloses support from the USDA (grant number FA9550-23-1-0606). R.L.-A. discloses support from BBSRC (grant number BB/Y008510/1), ERC (grant number DEUSBIO-949080) and Bezos Earth Fund (grant number BCSP/IC/001). M.S. discloses support from the Novo Nordisk Foundation (grant number NNF23OC0085919). S.R.S.P. discloses support from NSF (Graduate Research Fellowship). N.W. discloses support from the UK National Alternative Protein Innovation Centre NAPIC and Innovate UK Grant (grant number BB/Z516119/1). E.K. discloses support from Food@Stanford, Stanford SDSS Accelerator, NSF (grant number CMMI Award 2320933) and ERC (grant number 101141626).

Good Food Institute, Washington, DC, USA

Department of Civil & Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Singapore Institute of Food & Biotechnology Innovation, Agency for Science, Technology and Research, Singapore, Singapore

Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Department of Computer Science, Stanford University, Stanford, CA, USA

Department of Biomedical Engineering, Tufts University, Medford, MA, USA

Department of Bioengineering, Bezos Centre for Sustainable Protein, Microbial Food Hub and Centre for Engineering Biology, Imperial College London, London, UK

Rodrigo Ledesma-Amaro, Giorgia Del Missier & Lisa Neidhardt

Department of Chemical Engineering and Applied Chemistry and Vector Institute for Artificial Intelligence, University of Toronto, Toronto, Ontario, Canada

Department of Green Technology, University of Southern Denmark, Odense, Denmark

Department of Mechanical Engineering, Stanford University, Stanford, CA, USA

Skyler R. St. Pierre & Ellen Kuhl

USDA/NIFA AI Institute for Next-Generation Food Systems, University of California, Davis, Davis, CA, USA

School of Food Science and Nutrition, University of Leeds and National Alternative Protein Innovation Centre NAPIC, Leeds, UK

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B.D. and E.K. designed the layout. B.D., M.J.B., Y.C., K.G., D.J., D.L.K., R.L.-A., G.D.M., L.N., K.P., B.S.-L., M.S., S.R.S.P., I.T., A.T., N.W. and E.K. wrote and edited the paper.

The authors declare no competing interests.

Nature Food thanks Hongwei Zhang, Abdo Hassoun and Jiakai Lu for their contribution to the peer review of this work.

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Datta, B., Buehler, M.J., Chow, Y. et al. Artificial intelligence for food innovation. Nat Food (2026). https://doi.org/10.1038/s43016-026-01380-7

Version of record: 03 July 2026

DOI: https://doi.org/10.1038/s43016-026-01380-7