2025 July 20

Our publications (2023-present)




Food Informatics & General AI

  1. Leow, Z. M., Fu, C., Zhang, D.* Food Informatics: Leveraging data and knowledge to advance food systems. The Innovation Informatics, 2026. DOI: 10.59717/j.xinn-inform.2026.100038. (Invited)

  2. Ke, Q., Zhang, J., Huang, X., Kou, X., Zhang, D.* Machine learning unveils three layers of food complexity. npj Science of Food, 2026, 10, 87. DOI: 10.1038/s41538-026-00730-w.

  3. Zhang, D.* Practical guide for food scientists to build AI: Data, algorithms, and applications. Food Chemistry, 2026, 499, 147281. DOI: 10.1016/j.foodchem.2025.147281. (Invited)

  4. Zhang, D.*, Liu, M., Yu, Z., Xu, H., Pfister, S., Menichetti, G., … Rao, P. Domain knowledge, just evaluation, and robust data standards are required to advance AI in food science. Trends in Food Science & Technology, 2025, 105272. DOI: 10.1016/j.tifs.2025.105272.

  5. Xu, Y., et al. AI for science: Progress, challenges, and perspectives. The Innovation, 2026. DOI: 10.1016/j.xinn.2026.101530.

 

AI for Food Flavor and Formulation

  1. Zhang, J., Xing, H., Di Pizio, A., Ke, Q., Kou, X., Zhang, D.* Molecular atlas of key food odorants reveals structured aroma organization and enables generative aroma design. Advanced Science, 2026. accepted. DOI: 10.64898/2026.01.21.700072.

  2. Hou, X., Zhou, Z.*, Shi, P., Wang, Y., Liu, S., Ji, Z., Zhang, D.*, Mao, J. Machine learning reveals structural determinants of odor detection thresholds and identifies high-potency food odorants. npj Science of Food, 2026. accepted.

  3. Zhang, D.* From molecules to perception: A benchmark dataset for AI in sensory science. NeurIPS2025-AI4Science, 2025.

  4. Kou, X., Shi, P., Gao, C., Ma, P., Xing, H., Ke, Q., Zhang, D.* Data-driven elucidation of flavor chemistry. J Agric Food Chem, 2023, 71(18), 6789–6802. DOI: 10.1021/acs.jafc.3c00909.

  5. Zhang, J., Xu, J., Zhang, D., Huang, X., Ke, Q., Liu, H., Zhu, F., Yan, L. Distinguishing similar food aromas: EEG-based perception of tea and mint odors. Journal of Future Foods, 2026.

  6. Yu, X., Su, Z., Xia, G., Zhang, D., Li, H., Dong, W. Elucidating the interaction between pyrazine flavor compounds and coffee proteins: Insights from multiscale structural and molecular dynamics simulations. Current Research in Food Science, 2026, 13, 101478.

  7. Qian, J., Wang, X., Song, F., Liang, Y., Zhu, Y., Fang, Y., Zeng, W., Zhang, D., Dong, J. ChemSweet: An AI-driven computational platform for next-gen sweetener discovery. Food Chemistry, 2025, 463, 141362. DOI: 10.1016/j.foodchem.2024.141362.

 

AI for Food Safety

  1. Ye, T.#, Zhang, D.#, Xing, H#., … Hu, Q-N.*, Wu, A*. Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains. Journal of Advanced Research, 2026, 83,  129-139. DOI: 10.1016/j.jare.2025.08.045.

  2. Zhang, D.*, Liu, D., Jing, J., Jia, B., Tian, Y., Le, Y., Yu, Y., Hu, Q.-N. Unveiling the chemical complexity of food-risk components: A comprehensive data resource guide in 2024. Trends in Food Science & Technology, 2024, 148. DOI: 10.1016/j.tifs.2024.104513.

  3. Ji, J.#, Zhang, D.#, Ye, J., Zheng, Y., Cui, J., Sun, X. MycotoxinDB: A data-driven platform for investigating masked forms of mycotoxins. J Agric Food Chem, 2023, 71(24), 9501–9507. DOI: 10.1021/acs.jafc.3c01403.

  4. Zhang, H., Zhang, D.*, et al. Analysis of public opinion on food safety in Greater China with big data and machine learning. Current Research in Food Science, 2023, 6. DOI: 10.1016/j.crfs.2023.100468.

  5. Jiao, X., Zhu, J., Ye, W., Zou, H., Yan, B., Zhang, N., Qiang, J., Tao, Y., Zhang, H., Zhang, D.*, Fan, D. Artificial intelligence in smart seafood safety across the supply chains: Recent advances and future prospects. Trends in Food Science & Technology, 2025, 163. DOI: 10.1016/j.tifs.2025.105161.

  6. Cai, P., Liu, D., Xing, H., Zhang, D., Le, Y., Wu, A., Hu, Q. N. DeepMBEnzy: An AI-driven database of mycotoxin biotransformation enzymes. J Agric Food Chem, 2025. DOI: 10.1021/acs.jafc.5c02477.

  7. Zhang, S., Ye, Y., Jian, J., Zhang, D., Sun, X. A transfer learning approach to predict the combined toxicity of mycotoxins with limited data. Food Biosciences, 2025, 74, 108060.

 

AI for Nutrition and Health

  1. Zhao, C., Yang, S., He, F., He, K., Rao, P., Ke, L., Pang, B., Zhang, D.* Integrating multi-assay data for antioxidant discovery. ChemRxiv, 2026. DOI: 10.26434/chemrxiv.15005175/v1.

  2. Shi, P., Huang, X., Ke, Q., Kou, X., Zhang, D.* Mapping sleep-promoting volatiles in aromatic plants with machine learning: A comprehensive survey of 2,300 molecules. Digital Discovery, 2026, 5, 1068–1078. DOI: 10.1039/d5dd00173k. (Invited, cover story)

  3. Qiao, G., Zhang, D., Zhang, N., Shen, X., Jiao, X., Lu, W., Fan, D., Zhao, J., Zhang, H., Chen, W., Jin, Z. Food recommendation towards personalized wellbeing. Trends in Food Science & Technology, 2025, 156. DOI: 10.1016/j.tifs.2025.104877.

 

AI for Food Sustainability

  1. Zhang, D.*, Wang, Z., Oberschelp, C., Jing, H., Hellweg, S. Mapping the carbon footprint of chemicals in commerce. ChemRxiv, 2026. DOI: 10.26434/chemrxiv.15001530/v1.

  2. Qiu, Y.#, Zhang, D.#, Long, M., Zhou, Z., Gao, C., Ma, S., Qin, J., Chen, K., Chen, C., Zhao, Z., Deng, H. Coassembly of hybrid microscale biomatter for robust, water-processable, and sustainable bioplastics. Science Advances, 2025, 11(14), eadr1596. DOI: 10.1126/sciadv.adr1596.

  3. Zhang, D.*, Wang, Z., Oberschelp, C., Bradford, E., Hellweg, S. Enhanced deep-learning model for carbon footprints of chemicals. ACS Sustainable Chemistry & Engineering, 2024, 12(7), 2700–2708. DOI: 10.1021/acssuschemeng.3c07038.

  4. Ma, P., et al. The AI-material nexus rewiring the food supply chain: From molecule to market. The Innovation Life, 2026, 4, 100232.

  5. Wang, C., et al. Embodied artificial intelligence in the food supply chain: Innovations, challenges, and future perspectives. Trends in Food Science & Technology, 2026, 105698. DOI: 10.1016/j.tifs.2026.105698.

  6. Qiu, Y., Zhang, D., … Zhao, Z., Deng, H. Hierarchical assembly of biomass fiber-lamella-macromolecule networks for biocomposites with high strength and water-resistant sealing. PNAS, 2025. DOI: 10.1073/pnas.2521173122.

  7. Schmid, C., Kastner, F., Zhang, D., Langenberg, S., Hellweg, S. Spatiotemporal mapping of Swiss exterior wall material stock using a large language model and architectural history. Journal of Industrial Ecology, 2025. DOI: 10.1111/jiec.70058.

 

AI for Food Synthetic Biology and Processing

  1. Zhang, D., et al. Discovery of toxin-degrading enzymes with positive unlabeled deep learning. ACS Catalysis, 2024, 14, 3336–3348. DOI: 10.1021/acscatal.3c04461.

  2. Xing, H., ... Zhang, D.*, Hu, Q.-N. High-throughput prediction of enzyme promiscuity based on substrate–product pairs. Briefings in Bioinformatics, 2024, 25(2). DOI: 10.1093/bib/bbae089.

  3. Zhang, D.*, Jia, C., Sun, D., Gao, C., Fu, D., Cai, P., Hu, Q. N. Data-driven prediction of molecular biotransformations in food fermentation. J Agric Food Chem, 2023, 71(22), 8488–8496. DOI: 10.1021/acs.jafc.3c01172.

  4. Zhu, C. et al. A multi-modal pre-training framework-driven active learning system for enhanced protein evolution of CsCE. ACS Catalysis, 2026. DOI: 10.1021/acscatal.6c03864.

  5. Shi, P., Liu, S., Mao, J., Liu, X., Tu, R., Qin, H., Sun, A., Zhang, D., Mao, J.* AI-driven exploration of microbial resources in fermented foods. Trends in Food Science & Technology, 2026, 167, 105450. DOI: 10.1016/j.tifs.2025.105450.

  6. Ding, S., Tian, Y., Liu, D., Zhang, D., Xing, H., Chen, J., Liu, Z., Hu, Q. N. RxnCluster: A web-based tool for exploring reaction clusters leading to target molecules by digitalizing typical biosynthetic patterns. ACS Synthetic Biology, 2025.

  7. Tian, Y., Yang, L., Ding, S., Zhang, D., Yuan, L., Liu, Z., Hu, Q. N. BioTRY: A comprehensive knowledge base for titer, rate, and yield of biosynthesis. ACS Synthetic Biology, 2025, 14, 285–289.

  8. Ding, S., Liu, D., Tian, Y., Zhang, D., Xing, H., Chen, J., Liu, Z., Hu, Q. N. From reactants to products: computational methods for biosynthetic pathway design. Synthetic and Systems Biotechnology, 2025, 10(3), 1038–1049. DOI: 10.1016/j.synbio.2025.05.005.

  9. Cai, P., Liu, S., Zhang, D., Xing, H., Han, M., Liu, D., Gong, L., Hu, Q. N. SynBioTools: a one-stop facility for searching and selecting synthetic biology tools. BMC Bioinformatics, 2023, 24(1), 152. DOI: 10.1186/s12859-023-05281-5.

  10. Cai, P., Liu, S., Zhang, D., Hu, Q. N. MCF2Chem: A manually curated knowledge base of biosynthetic compound production. Biotechnology for Biofuels and Bioproducts, 2023, 16(1), 167. DOI: 10.1186/s13068-023-02419-8.

  11. Zhang, D., Xing, H., Liu, D., Han, M., Cai, P., Lin, H., et al. Deep learning enables rapid identification of mycotoxin-degrading enzymes. ChemRxiv, 2023. DOI: 10.26434/chemrxiv-2023-g6qb5.

 

Before 2023
https://orcid.org/0000-0003-2467-6286