Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development

Comprehensive Reviews in Food Science and Food Safety, Год журнала: 2025, Номер 24(1)

Опубликована: Янв. 1, 2025

Abstract The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies AI, researchers can explore and develop new substances in digital environment, saving time resources. More more research will use AI big data to enhance product flavor, improve quality, meet consumer needs, drive industry toward smarter sustainable future. In this review, we elaborate mechanisms recognition their potential impact nutritional regulation. With increase accumulation development internet information technology, databases ingredient have made great progress. These provide detailed content, molecules, chemical properties various compounds, providing valuable support for rapid evaluation components construction screening technology. popularization fields, field has also ushered opportunities. This review explores role enhancing analysis through high‐throughput omics technologies. algorithms offer pathway scientifically formulations, thereby customized meals. Furthermore, it discusses safety challenges into industry.

Язык: Английский

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AI in single-atom catalysts: a review of design and applications

Journal of Materials Informatics, Год журнала: 2025, Номер 5(1)

Опубликована: Фев. 12, 2025

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances activity, selectivity, and stability. SACs demonstrate substantial promise electrocatalysis applications, such fuel cells, CO2 reduction, hydrogen production, due to ability maximize utilization of active sites. However, the development efficient stable involves intricate design screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) neural networks (NNs), offers powerful tools for accelerating discovery optimization SACs. This review systematically discusses application AI technologies through four key stages: (1) Density functional theory (DFT) ab initio molecular dynamics (AIMD) simulations: DFT AIMD are used investigate mechanisms, with high-throughput applications expanding accessible datasets; (2) Regression models: ML regression models identify features that influence performance, streamlining selection promising materials; (3) NNs: NNs expedite known structural models, facilitating rapid assessment potential; (4) Generative adversarial (GANs): GANs enable prediction novel high-performance tailored specific requirements. work provides comprehensive overview current status insights recommendations future advancements field.

Язык: Английский

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Guided diffusion for inverse molecular design

Nature Computational Science, Год журнала: 2023, Номер 3(10), С. 873 - 882

Опубликована: Окт. 5, 2023

Язык: Английский

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Machine learning in process systems engineering: Challenges and opportunities

Computers & Chemical Engineering, Год журнала: 2023, Номер 181, С. 108523 - 108523

Опубликована: Ноя. 22, 2023

Язык: Английский

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MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Advanced Materials, Год журнала: 2023, Номер 36(6)

Опубликована: Окт. 10, 2023

Abstract Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, informatics is continuously accelerating the vigorous development of new materials. The emergence “GPT (Generative Pre‐trained Transformer) AI” shows that scientific research field has entered era intelligent civilization with “data” as basic factor “algorithm + computing power” core productivity. continuous innovation AI will impact cognitive laws methods, reconstruct knowledge wisdom system. This leads to think more about informatics. Here, a comprehensive discussion models infrastructures provided, advances in discovery design are reviewed. With rise paradigms triggered by “AI for Science”, vane informatics: “MatGPT”, proposed technical path planning from aspects data, descriptors, generative models, pretraining directed collaborative training, experimental robots, well efforts preparations needed develop generation informatics, carried out. Finally, challenges constraints faced discussed, order achieve digital, intelligent, automated construction joint interdisciplinary scientists.

Язык: Английский

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Allosteric drugs: New principles and design approaches

Current Opinion in Structural Biology, Год журнала: 2024, Номер 84, С. 102758 - 102758

Опубликована: Янв. 2, 2024

Язык: Английский

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Geometry-complete diffusion for 3D molecule generation and optimization

Communications Chemistry, Год журнала: 2024, Номер 7(1)

Опубликована: Июль 3, 2024

Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such are unable to learn important geometric properties of molecules, as they adopt molecule-agnostic and non-geometric GNNs their networks, which notably hinders ability generate valid large molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) molecule generation, outperforms existing molecular models significant margins across conditional unconditional settings QM9 dataset larger GEOM-Drugs dataset, respectively. Importantly, demonstrate that GCDM’s generative process enables model proportion energetically-stable at scale GEOM-Drugs, whereas previous fail do so with features learn. Additionally, show extensions GCDM can not only effectively design specific protein pockets but be repurposed consistently optimize geometry chemical composition stability property specificity, demonstrating new versatility models. Code data freely available on GitHub .

Язык: Английский

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Directional multiobjective optimization of metal complexes at the billion-system scale

Nature Computational Science, Год журнала: 2024, Номер 4(4), С. 263 - 273

Опубликована: Март 29, 2024

Язык: Английский

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Has generative artificial intelligence solved inverse materials design?

Matter, Год журнала: 2024, Номер 7(7), С. 2355 - 2367

Опубликована: Июль 1, 2024

The directed design and discovery of compounds with pre-determined properties is a long-standing challenge in materials research. We provide perspective on progress toward achieving this goal using generative models for chemical compositions crystal structures based set powerful statistical techniques drawn from the artificial intelligence community. introduce central concepts underpinning crystalline materials. Coverage provided early implementations inorganic crystals adversarial networks variational autoencoders through to ongoing involving autoregressive diffusion models. influence choice representation architecture discussed, along metrics quantifying quality hypothetical produced. While further developments are required enable realistic predictions richer structure property datasets, already proving be complementary traditional strategies.

Язык: Английский

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Molecular similarity: Theory, applications, and perspectives

Artificial Intelligence Chemistry, Год журнала: 2024, Номер 2(2), С. 100077 - 100077

Опубликована: Авг. 31, 2024

Molecular similarity pervades much of our understanding and rationalization chemistry. This has become particularly evident in the current data-intensive era chemical research, with measures serving as backbone many Machine Learning (ML) supervised unsupervised procedures. Here, we present a discussion on role molecular drug design, space exploration, "art" generation, representations, more. We also discuss more recent topics similarity, like ability to efficiently compare large libraries.

Язык: Английский

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