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Ecotoxicology

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What is the ecotoxicity of a given chemical for a given aquatic species? Predicting interactions between species and chemicals using recommender system techniques.

SAR and QSAR in environmental research
Ecotoxicological safety assessment of chemicals requires toxicity data on multiple species, despite the general desire of minimizing animal testing. Predictive models, specifically machine learning (ML) methods, are one of the tools capable of solvin...

Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence.

The Science of the total environment
Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a...

Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE).

Journal of hazardous materials
Exposure to toxic chemicals threatens species and ecosystems. This study introduces a novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data, providing an alternative to complement traditional in vivo ecotoxicity testing...

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Environment international
Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-cal...

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

Environmental science & technology
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies u...

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions.

The Science of the total environment
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex a...

Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction.

Journal of hazardous materials
The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecologic...

Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies.

Toxicology mechanisms and methods
New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relation...

Impacts of micro/nano plastics on the ecotoxicological effects of antibiotics in agricultural soil: A comprehensive study based on meta-analysis and machine learning prediction.

The Science of the total environment
Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antib...

PE-GCL: Advancing pesticide ecotoxicity prediction with graph contrastive learning.

Journal of hazardous materials
Ecotoxicity assessments, which rely on animal testing, face serious challenges, including high costs and ethical concerns. Computational toxicology presents a promising alternative; nevertheless, existing predictive models encounter difficulties such...