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Updated: Jun 9, 2026
05:47
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Green toxicology only becomes beautiful through AI
Alexandra Maertens1, Thomas Hartung1,2,3
1Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, United States.
Frontiers in Chemistry
|June 8, 2026
Summary
Green Toxicology integrates toxicological foresight into chemical design to prevent hazards. Artificial intelligence (AI) combined with new approach methodologies (NAMs) offers a scalable, predictive framework for sustainable chemistry and public health protection.
Area of Science:
- Environmental Science
- Toxicology
- Computational Chemistry
Background:
- Green Toxicology applies Green Chemistry principles to anticipate and prevent chemical hazards.
- Key pillars include prevention, precaution, life-cycle thinking, and avoiding regrettable substitutions.
- Current limitations involve fragmented data, slow regulatory acceptance, and validation challenges for New Approach Methodologies (NAMs).
Purpose of the Study:
- To explore the transformative potential of Artificial Intelligence (AI) in advancing Green Toxicology.
- To outline a practical framework integrating AI and NAMs for sustainable chemical innovation.
- To address challenges in data integration, predictive accuracy, and risk assessment within Green Toxicology.
Main Methods:
- Leveraging deep learning, natural language processing, and explainable AI to analyze legacy toxicological data.
- Integrating AI with microphysiological systems and omics for predictive, human-relevant assessments.
- Developing probabilistic risk assessment models enabled by AI.
Main Results:
- AI can effectively integrate heterogeneous datasets for enhanced predictive toxicology.
- AI facilitates the mining of existing studies and linking of adverse outcome pathways.
- AI enables the proactive design of safer chemicals and scalable risk assessments.
Conclusions:
- AI offers a transformative solution to overcome current limitations in Green Toxicology.
- The integration of AI, NAMs, and omics creates a predictive and scalable framework for sustainable chemistry.
- This approach reconciles industrial needs with ecological integrity and public health protection.
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