AI for mixture toxicity assessment

Data-driven pollutant and toxicant identification

Integration of high-throughput bioassays with HRMS analysis

Regional aquatic toxicant prioritization (e.g., Pearl River Delta, shale gas exploitation areas)

Cheng, F. et al. Event-driven taxonomy (EDT) screening: Leveraging effect-based spectral libraries to accelerate semiquantitative nontarget analysis of AhR agonists in sediment in the era of big data. Environ. Sci. Technol. 59, 14359–14371 (2025).

Cheng, F. et al. Deep learning bridged bioactivity, structure and HRMS-readable evidence to decipher non-target toxicants in complex mixtures. Environ. Sci. Technol. 58, 15415–15427 (2024).

Cheng, F. et al. Text mining-based suspect screening for aquatic risk assessment in the big data era: Event driven taxonomy links chemical exposures and hazards. Environ. Sci. Technol. Lett. 10, 1004–1010 (2023).

Cheng, F. et al. Data-driven endpoint selection in data-poor scenarios: Bioassay design for shale gas flowback and produced waters. Environ. Sci. Technol. Lett. 9, 1074–1080 (2022).

Cheng, F., Li, H., Brooks, B. W., & You, J. Signposts for aquatic toxicity evaluation in China: Text mining using event-driven taxonomy within and among regions. Environ. Sci. Technol. 55, 8977–8986 (2021).

Cheng, F., Li, H., Brooks, B. W., & You, J. Retrospective risk assessment of chemical mixtures in the big data era. Environ. Sci. Technol. 54, 5925–5927 (2020).

He, L.; Cheng, F.*; You, J*. et al. Identifying and prioritizing organic toxicants in treated flowback and produced water… Water Res. 277, 123311 (2025).

Huang, J. H.#; Cheng, F.# et al. Effect driven prioritization of contaminants in wastewater treatment plants across China… Water Res. 264, 122223 (2024).