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Updated: Feb 1, 2026

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Unraveling structure-Toxicity relationships of PFAS: Insights from quantum chemical descriptors and QSAR models
Zhiwen Cheng1, Jie Zhou2, Xin Peng3
1Faculty of Chemical Engineering and Energy Technology, Shanghai Institute of Technology, Shanghai 201418, PR China; Sichuan Provincial Engineering Research Center of City Solid Waste Energy and Building Materials Conversion and Utilization Technology, Chengdu University, Chengdu 610106, PR China.
Abstract:
Assessing the ecotoxicity of per- and polyfluoroalkyl substances (PFAS) is essential for understanding their environmental behavior and potential risks. However, systematic evaluations linking PFAS toxicity to molecular-level characteristics remain limited. Here, quantitative structure-activity relationship (QSAR) models were developed to elucidate the relationships between acute and chronic toxicity thresholds for fish, Daphnid, and green algae across 26 PFAS and their quantum chemical descriptors. The ECOSAR-based results demonstrated that both acute and chronic toxicity toward the three aquatic species increased with the number of -CF2- moieties, indicating a structure-dependent toxicity trend. The developed QSAR models exhibited excellent stability, robustness, and predictive performance (R2 > 0.9, q2 > 0.9, and Qext2 > 0.9). Moreover, the number of -CF2- moieties and the Fukui index with respect to electrophilic attack [f(-)] were identified as intrinsic factors governing PFAS toxicity. Overall, this work provides molecular-level insight into the structural determinants of PFAS toxicity and establishes a reliable theoretical framework for predicting the toxicological behavior of emerging PFAS.
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