07/2024 ~ to present     Postdoc, Institute of Reproductive and Child Health, School of Public Health, Peking University, Beijing, P. R. China

01/2024 ~ 04/2024        International exchange, Aarhus University, Denmark

09/2018 ~ 07/2024       PhD, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, P.R. China

09/2015 ~ 07/2018        ME, College of Environmental Science and Engineering, Tongji University, Shanghai, P.R. China

09/2010 ~ 07/2014        BS, College of Environment, Henan Normal University

My research focused on structure-activity relationships. Based on the adverse outcome pathway framework, machine learning methods were adopted to predict the toxic effects of chemicals at different biological levels. Deep learning and non-target analysis were employed to solve the hot issues in the field of environment and health. As the first author, the related studies have been published in prestigious journals, e.g., J Hazard Mater and Sci Total Environ. Our recent work is to develop multimodal models by integrating cheminformatics and bioinformatics. The established model is used to predict the targets of compounds and elucidate their potential molecular mechanisms of action.

Environment health, Artificial intelligence

1. Zhao L, Xue Q^*, Zhang HZ, Hao YX, Yi H, Liu X, Pan WX, Fu JJ, Zhang AQ^*. CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals. J Hazard Mater. 2024, 465: 133055.

2. Zhao L, Wang R^*, Zhang C, Yin DQ, Yang SY, Huang XT, Geochemical controls on the distribution of mercury and methylmercury in sediments of the coastal East China Sea. Sci Total Environ. 2019, 667: 133-141.

3. Wang LG, Zhao L, Liu X^*, Fu JJ, Zhang AQ^*, SepPCNET: Deeping learning on a 3D surface electrostatic potential point cloud for enhanced toxicity classification and its application to suspected environmental estrogens. Environ Sci Technol. 2021, 55(14): 9958–9967.

4. Zhang HZ, Yi H, Hao YX, Zhao L, Pan WX, Xue Q, Liu X^*, Fu JJ, Zhang AQ^*. Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards. J Hazard Mater. 2024, 465: 133092.

5. Wang R, Zhang C, Huang XT, Zhao L, Yang SY, Struck U, Yin DQ^*, Distribution and source of heavy metals in the sediments of the coastal East China Sea: Geochemical controls and typhoon impact. Environ Pollut. 2020, 260: 113936.