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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.

Jordi Minnema¹, Markus Viljanen¹, Emiel Rorije¹

¹National Institute for Public Health and the Environment (RIVM), P.O. Box 1, 3720 BA Bilthoven, The Netherlands.

Chemical Research in Toxicology

|June 14, 2026

Summary

This summary is machine-generated.

Machine learning (ML) models predict chemical ecotoxicity for aquatic species, aiding Species Sensitivity Distributions (SSDs) in environmental risk assessment. Qualitative validation and regulatory trust are key for integrating these AI tools.

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Area of Science:

Environmental toxicology
Computational toxicology
Ecotoxicology

Background:

Chemical risk assessment faces challenges due to limited toxicological data for numerous substances.
Artificial intelligence (AI) and machine learning (ML) offer potential solutions for data gaps via predictive modeling.
Limited integration of ML into regulatory risk assessment stems from a lack of trust in model outcomes.

Purpose of the Study:

To develop and validate an ML model for predicting chemical ecotoxicity across aquatic species.
To utilize ML-predicted ecotoxicity data for constructing Species Sensitivity Distributions (SSDs).
To address the critical need for regulatory trust in ML-derived SSDs for effective chemical risk assessment.

Main Methods:

Development of a machine learning model to predict chemical ecotoxicity.
Application of model predictions to generate Species Sensitivity Distributions (SSDs).
Quantitative performance evaluation and extensive qualitative validation (interpretability, transparency) of the ML model and SSDs.

Main Results:

Successfully developed an ML model predicting ecotoxicity for diverse aquatic species.
Generated SSDs using ML predictions, demonstrating their utility in environmental risk assessment.
Highlighted the importance of qualitative validation and regulatory acceptance for ML-based SSDs.

Conclusions:

ML models show promise for predicting ecotoxicity and constructing SSDs, crucial for chemical risk assessment.
Enhancing regulatory trust through transparency and interpretability is vital for adopting ML in risk assessment.
Interdisciplinary collaboration is essential to bridge the gap between AI advancements and regulatory needs in chemical safety evaluation.