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A Screening Method to Identify Potential Endocrine Disruptors Using Chemical Toxicity Big Data and a Deep Learning Model with a Focus on Cleaning and Laundry Products
Summary
Researchers used toxicity databases and deep learning models to screen cleaning and laundry products for potential endocrine-disrupting chemicals (EDCs). The model identified several compounds in common household products that are likely EDCs, many of which are also associated with plastic packaging or plastic-based product formulations. This approach could accelerate the identification of hazardous chemicals in consumer products without animal testing.
The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants.