2025 July 20

Our publications




2026

  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. bioRxiv, 2026. DOI: 10.64898/2026.01.21.700072.
  2. Zhao, C., Yang, S., He, F., He, K., Rao, P., Ke, L., Pang, B, and Zhang, D. Integrating multi-assay data for antioxidant discovery. ChemRxiv. 2026. DOI: 10.26434/chemrxiv.15005175/v1
  3. 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.
  4. 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.
  5. 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)
  6. 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)
  7. Leow, Z. M., Fu, C., Zhang, D.* Food Informatics: Leveraging data and knowledge to advance food systems. The Innovation Informatics, 2026 accepted. https://doi.org/10.59717/j.xinn-inform.2026.100038 (Invited)
  8. Xu, Y., et al. AI for science: Progress, challenges, and perspectives. The Innovation, 2026 accepted.
  9. Ma, P., et al. The AI-material nexus rewiring the food supply chain: From molecule to market. The Innovation Life, 2026, 4, 100232.
  10. 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.
  11. Wang, C., ..., Zhang, D., Fan, D., Wang, W., Ma, P.*, Wang, F.* 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
  12. 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.
  13. 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.

 

2025

  1. 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.
  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. 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 2025. DOI: 10.1016/j.jare.2025.08.045.
  4. 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.
  5. 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.
  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. Proceedings of the National Academy of Sciences of the United States of America (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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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. DOI: 10.1021/acs.jafc.5c02477.
  13. 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.
  14. Zhang, D.*, From molecules to perception: A benchmark dataset for AI in sensory science, NeurIPS2025-AI4Science, 2025.

 

2023-2024

  1. 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.
  2. Zhang, D.; Xing, H.; Liu, D.; Han, M.; Cai, P.; Lin, H.; Tian, Y.; Guo, Y.; Sun, B.; Le, Y.; et al. Discovery of Toxin-Degrading Enzymes with Positive Unlabeled Deep Learning. ACS Catalysis 2024, 14, 3336-3348. DOI: 10.1021/acscatal.3c04461.
  3. 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.
  4. Xing, H.; Cai, P.; Liu, D.; Han, M.; Liu, J.; Le, Y.; 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.
  5. 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.
  6. 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. 
  7. 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.
  8. Zhang, H.; Zhang, D.*; Wei, Z.; Li, Y.; Wu, S.; Mao, Z.; He, C.; Ma, H.; Zeng, X.; Xie, X.; 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.
  9. Xing, H.; Zhang, D.; Cai, P.; Zhang, R.; Hu, Q. N. RDBridge: a knowledge graph of rare diseases based on large-scale text mining. Bioinformatics 2023, 39 (7). DOI: 10.1093/bioinformatics/btad440.
  10. 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.
  11. Cai, P.; Liu, S.; Zhang, D.; Hu, Q. N. MCF2Chem: A manually curated knowledge base of biosynthetic compound production. Biotechnol Biofuels Bioprod 2023, 16 (1), 167. DOI: 10.1186/s13068-023-02419-8.
  12. 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