Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning

Lang Lei; Liangmao Zhang; Zhibang Han; Qirui Chen; Pengcheng Liao; Dong Wu; Jun Tai; Bing Xie; Yinglong Su

doi:10.1016/j.envpol.2023.123093

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning

Environ Pollut. 2024 Feb 1:342:123093. doi: 10.1016/j.envpol.2023.123093. Epub 2023 Dec 8.

Authors

Lang Lei¹, Liangmao Zhang¹, Zhibang Han¹, Qirui Chen¹, Pengcheng Liao¹, Dong Wu², Jun Tai³, Bing Xie⁴, Yinglong Su⁵

Affiliations

¹ Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China.
² Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
³ Shanghai Environmental Sanitation Engineering Design Institute Co., Ltd., Shanghai, 200232, China.
⁴ Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
⁵ Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China. Electronic address: ylsu@des.ecnu.edu.cn.

PMID: 38072027
DOI: 10.1016/j.envpol.2023.123093

Abstract

The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R² = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10^-3 to 10² mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.

Keywords: Ecological risk; Graph attention network; Molecular descriptor; Molecular fingerprint; Species sensitivity distribution; XGBoost.

MeSH terms

Environmental Pollutants*
Machine Learning*
Quantitative Structure-Activity Relationship
Reproducibility of Results
Risk Assessment

Substances

Environmental Pollutants