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  11. Single-task Regression Naturally Adapts To Multi-species (eco)toxicological Modelling: A Case Study On Animals

Single-task regression naturally adapts to multi-species (eco)toxicological modelling: a case study on animals

Suyu Mei1

  • 1Software College, Shenyang Normal University, Shenyang, 110034, China. meisygle@outlook.com.

Environmental Science and Pollution Research International
|February 1, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel single-task regression approach for in silico ecotoxicological modeling, improving multi-species toxicity predictions. The method enhances data augmentation and inter-species pattern transfer, outperforming existing models.

Area of Science:

  • Computational toxicology and environmental science.
  • Development of predictive models for chemical safety assessment.

Background:

  • In silico (eco)toxicological modeling is crucial for assessing chemical risks to ecosystems, animals, and humans.
  • Current local and multi-task models have limitations in multi-species applicability and require common data points across species.

Purpose of the Study:

  • To propose a single-task regression strategy for adaptable multi-species (eco)toxicological modeling.
  • To overcome the limitations of existing models in handling diverse species datasets without common pesticides.

Main Methods:

  • Aggregated 37,305 ecotoxicological measurements for 29,140 pesticides across 10 animal groups.
  • Trained four machine learning models: extreme gradient boosting (XGBoost), deep neural networks (DNN), random forest (RF), and support vector regression (SVR).
Keywords:
Chemical fingerprintsIn silico (eco)toxicological modellingMulti-task regressionQSAR

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  • Employed five-fold stratified cross-validation to evaluate model performance.

    • Main Results:

    • XGBoost demonstrated superior performance with R² of 0.67, RMSE of 0.44, and MAE of 0.29.
    • The single-task regression model achieved a significant R² increase of 0.08–0.49 compared to local models.
    • Morgan bit 389 (a five-atom aromatic ring fragment) was identified as a key predictor by XGBoost.

    Conclusions:

    • The proposed single-task regression strategy effectively adapts ecotoxicological modeling to multiple species.
    • This approach facilitates data augmentation and inter-species knowledge transfer, enhancing predictive accuracy.
    • Case studies confirmed the model's credibility by analyzing toxicity and structural similarities of pesticides.
    Single-task regression
    Transfer learning