AIMC Topic: Ecotoxicology
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A Horizon Scan to Support Chemical Pollution-Related Policymaking for Sustainable and Climate-Resilient Economies.
Environmental toxicology and chemistry
May 8, 2023
While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical chall...
Will Artificial Intelligence be Useful (or Misused) in Environmental Toxicology and Chemistry?
Environmental toxicology and chemistry
Mar 10, 2023
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives.
Journal of hazardous materials
Jun 27, 2022
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in t...
New Models to Predict the Acute and Chronic Toxicities of Representative Species of the Main Trophic Levels of Aquatic Environments.
Molecules (Basel, Switzerland)
Nov 19, 2021
To assess the impact of chemicals on an aquatic environment, toxicological data for three trophic levels are needed to address the chronic and acute toxicities. The use of non-testing methods, such as predictive computational models, was proposed to ...
Accelerating the pace of ecotoxicological assessment using artificial intelligence.
Ambio
Aug 24, 2021
Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity dat...
Support vector machine-based model for toxicity of organic compounds against fish.
Regulatory toxicology and pharmacology : RTP
Apr 30, 2021
Predicting the toxicity of chemicals to various fish species through chemometric approach is crucial for ecotoxicological assessment of existing as well as not yet synthesized chemicals. This paper reports a quantitative structure-activity/toxicity r...
Development of Deep Learning Models for Predicting the Effects of Exposure to Engineered Nanomaterials on Daphnia magna.
Small (Weinheim an der Bergstrasse, Germany)
Jun 17, 2020
This study presents the results of applying deep learning methodologies within the ecotoxicology field, with the objective of training predictive models that can support hazard assessment and eventually the design of safer engineered nanomaterials (E...
Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory.
Chemosphere
Aug 16, 2019
Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically ...
Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.
Environmental pollution (Barking, Essex : 1987)
Jul 6, 2019
Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human health. Quantitative structure-activity relationship (QSAR) models are a promising alternative to i...
Machine Learning for Environmental Toxicology: A Call for Integration and Innovation.
Environmental science & technology
Oct 19, 2018
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