Identification and prioritization of environmental organic pollutants: from an analytical and toxicological perspective
Exposure to environmental organic pollutants has triggered significant ecological impacts
and adverse health outcomes, which have been received substantial and increasing …
and adverse health outcomes, which have been received substantial and increasing …
Artificial intelligence-based toxicity prediction of environmental chemicals: future directions for chemical management applications
Recently, research on the development of artificial intelligence (AI)-based computational
toxicology models that predict toxicity without the use of animal testing has emerged …
toxicology models that predict toxicity without the use of animal testing has emerged …
Dual use of artificial-intelligence-powered drug discovery
Dual use of artificial-intelligence-powered drug discovery | Nature Machine Intelligence Skip to
main content Thank you for visiting nature.com. You are using a browser version with limited …
main content Thank you for visiting nature.com. You are using a browser version with limited …
Machine learning and artificial intelligence in toxicological sciences
Abstract Machine learning and artificial intelligence approaches have revolutionized
multiple disciplines, including toxicology. This review summarizes representative recent …
multiple disciplines, including toxicology. This review summarizes representative recent …
Artificial intelligence (AI)—it's the end of the tox as we know it (and I feel fine)
N Kleinstreuer, T Hartung - Archives of Toxicology, 2024 - Springer
The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
the potential to transform chemical safety evaluation. Toxicology has evolved from an …
STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity
Background: Modern chemical toxicology is facing a growing need to Reduce, Refine, and
Replace animal tests for hazard identification. The most common type of animal assays for …
Replace animal tests for hazard identification. The most common type of animal assays for …
Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge-based deep neural network approach
Traditional experimental testing to identify endocrine disruptors that enhance estrogenic
signaling relies on expensive and labor-intensive experiments. We sought to design a …
signaling relies on expensive and labor-intensive experiments. We sought to design a …
Review of machine learning and deep learning models for toxicity prediction
The ever-increasing number of chemicals has raised public concerns due to their adverse
effects on human health and the environment. To protect public health and the environment …
effects on human health and the environment. To protect public health and the environment …
VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules
M Di Stefano, S Galati, L Piazza… - Journal of Chemical …, 2023 - ACS Publications
The application of artificial intelligence and machine learning (ML) methods is becoming
increasingly popular in computational toxicology and drug design; it is considered as a …
increasingly popular in computational toxicology and drug design; it is considered as a …
[HTML][HTML] Predicting chemical hazard across taxa through machine learning
J Wu, S D'Ambrosi, L Ammann… - Environment …, 2022 - Elsevier
We applied machine learning methods to predict chemical hazards focusing on fish acute
toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup …
toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup …