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Accelerating the pace of ecotoxicological assessment using artificial intelligence.

Runsheng Song1, Dingsheng Li2, Alexander Chang3

  • 1Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA, 98121, USA.

Ambio
|August 24, 2021
PubMed
Summary

This study introduces a new method using Artificial Neural Networks (ANN) to create Species Sensitivity Distributions (SSDs) for more chemicals. This expands ecotoxicological risk assessment by predicting toxicity data for thousands of compounds.

Keywords:
Chemical toxicityEnvironmental toxicityLife cycle assessmentMachine learningQSAR

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Area of Science:

  • Environmental Toxicology
  • Computational Chemistry
  • Ecotoxicology

Background:

  • Species Sensitivity Distributions (SSDs) are crucial for assessing chemical ecotoxicological risks.
  • Limited experimental toxicity data restricts the development of SSDs for most chemicals.
  • Existing SSDs cover only a small fraction of chemicals, hindering comprehensive risk assessment.

Purpose of the Study:

  • To develop a novel approach for expanding the chemical coverage of SSDs.
  • To leverage Artificial Neural Networks (ANN) to predict ecotoxicity data.
  • To generate a comprehensive SSD database for screening-level ecotoxicological assessments.

Main Methods:

  • Collected over 2000 experimental Lethal Concentration 50 (LC50) toxicity data points for 8 aquatic species.
  • Trained Artificial Neural Network (ANN) models for each species based on molecular structure to predict LC50 values.
  • Applied predicted LC50 values to fit SSD curves using a bootstrapping method for 8424 chemicals.

Main Results:

  • Developed ANN models with R-squared values ranging from 0.54 to 0.75 (median R² = 0.69).
  • Successfully generated SSDs for 8424 chemicals from the ToX21 database.
  • Demonstrated a significant expansion of chemical coverage for SSD analysis.

Conclusions:

  • The ANN-based approach effectively expands the availability of SSDs for a large number of chemicals.
  • The generated SSD database provides a valuable screening-level resource for ecotoxicological impact assessment.
  • This method addresses the data scarcity issue, enabling broader chemical risk evaluation.