Rapid Prediction of Chemical Ecotoxicity through Genetic Algorithm Optimized Neural Network Models

Ping Hou, Bu Zhao, Olivier Jolliet, Ji Zhu, Peng Wang, Ming Xu

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Evaluating potentially hazardous effects of chemicals on ecosystems has always been an important research topic traditionally studied using laboratory or field experiments. Experiment-based ecotoxicity test results are only available for a limited number of chemicals due to the extensive experimental effort and cost. Given the ever-increasing number of chemicals involved in the modern production process and products, rapidly characterizing chemical ecotoxicity at lower costs has become critical for guiding technology and policy development for chemical risk management. In this study, artificial neural network models are developed to predict chemical ecotoxicity (HC50) based on experimental data to fill data gaps in a widely used database (USEtox). To reduce the manual tuning effort on optimal network architecture, a genetic algorithm is investigated to automatically search and configure the network architecture. The resulting neural network model reached an average test R2 of 0.632 and had a trivial difference with the global optimal regarding validation MSE. The findings of this study can rapidly predict the ecotoxicity of chemicals and further help to understand the potential risk of chemicals and develop strategies for risk management.

Original languageEnglish
Pages (from-to)12168-12176
Number of pages9
JournalACS Sustainable Chemistry and Engineering
Volume8
Issue number32
DOIs
StatePublished - Aug 17 2020

Bibliographical note

Publisher Copyright:
© 2020 American Chemical Society.

Keywords

  • Chemical risk assessment
  • Hazardous concentration
  • Machine learning
  • Neural network
  • USEtox

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry
  • General Chemical Engineering
  • Renewable Energy, Sustainability and the Environment

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