AIMC Topic: Endocrine Disruptors
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CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.
Journal of hazardous materials
Nov 22, 2023
Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing res...
Modeling and insights into the structural characteristics of endocrine-disrupting chemicals.
Ecotoxicology and environmental safety
Jul 13, 2023
Endocrine-disrupting chemicals (EDCs) can cause serious harm to human health and the environment; therefore, it is important to rapidly and correctly identify EDCs. Different computational models have been proposed for the prediction of EDCs over the...
Thyroid endocrine disruption and hepatotoxicity induced by bisphenol AF: Integrated zebrafish embryotoxicity test and deep learning.
The Science of the total environment
Feb 4, 2022
Bisphenol AF (BPAF) is an emerging contaminant prevalent in the environment as one of main substitutes of bisphenol A (BPA). It was found that BPAF exhibited estrogenic effects in zebrafish larvae in our previous study, while little is known about it...
Graphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies.
The Science of the total environment
Oct 29, 2021
In our previous studies, it was found that graphene oxide (GO) reduced the endocrine disruption of bisphenol A (BPA) in zebrafish embryo and larvae, but through different mechanisms. In this study, adult male zebrafish were selected to further unders...
Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors.
Journal of chemical information and modeling
Apr 19, 2021
This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a s...
Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.
Laboratory investigation; a journal of technical methods and pathology
Aug 10, 2020
As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)popula...
A curated knowledgebase on endocrine disrupting chemicals and their biological systems-level perturbations.
The Science of the total environment
Jul 16, 2019
Human well-being can be affected by exposure to several chemicals in the environment. One such group is endocrine disrupting chemicals (EDCs) that can perturb the hormonal homeostasis leading to adverse health effects. In this work, we have developed...
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 Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project.
Journal of chemical information and modeling
Feb 11, 2019
The nuclear androgen receptor (AR) is one of the most relevant biological targets of Endocrine Disrupting Chemicals (EDCs), which produce adverse effects by interfering with hormonal regulation and endocrine system functioning. This paper describes n...
Machine learning models for predicting endocrine disruption potential of environmental chemicals.
Journal of environmental science and health. Part C, Environmental carcinogenesis & ecotoxicology reviews
Jan 10, 2019
We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox mode...
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