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Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models
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Researchers evaluated polyurethane foams made from bio-derived polyester polyols under ISO 20200 composting conditions, finding that these bio-based formulations lost an average of 25.4% of their mass after 45 days — significantly outperforming conventional polyether-based polyurethanes in biodegradability, with additives like carbon black and lignin providing no acceleration benefit.
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identifying three distinct categories represented by Dimethylhydantoin, Phenol, and Dicyclohexyl phthalate. Our analysis included physicochemical properties, target prediction, and KEGG and GO pathway analyses, revealing diverse and complex mechanisms of toxicity. Given the complexity of these mechanisms, traditional molecule-target research approaches proved insufficient. Support Vector Machines (SVMs) combined with molecular descriptors achieved an accuracy of 0.85 in the test dataset, while our custom deep learning model, integrating molecular SMILES and graphs, achieved an accuracy of 0.88 in the test dataset. These models effectively predicted reproductive toxicity, highlighting the potential of computational methods in pharmaceutical safety evaluation. Our study provides a robust framework for utilizing computational methods to enhance the safety evaluation of potential pharmaceutical compounds.
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Researchers used molecular docking and dynamics simulations to assess the reproductive toxicity risk of 22 phthalate esters against key proteins in the hypothalamic-pituitary-gonadal axis, producing a prioritized hazard ranking that identifies which phthalates pose the greatest endocrine disruption risk to reproductive health.
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Researchers reviewed six decades of computational approaches for predicting environmental persistence of contaminants, tracing the evolution from classical QSAR models to modern machine learning methods, and proposed a practical roadmap for applying these tools to small molecules, polymers, and microplastics in decision-ready regulatory contexts.
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Researchers used computer modeling to assess the toxicity of phthalate and bisphenol plasticizer chemicals, which leach from plastics into food and the environment. These computational tools can help predict which plastic additives pose the greatest risks to human health without extensive animal testing.
Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis
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Researchers used machine learning techniques to build adverse outcome pathways for understanding how phthalate esters, commonly used as plasticizers in plastics, may disrupt the human endocrine system. The study identified key molecular features and biological events that influence endocrine disruption effects, providing a framework for assessing the potential health impacts of phthalate exposure.
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This study developed GELATO, a computational framework integrating transcriptomics with protein-protein interaction networks and machine learning to investigate how endocrine disruptors from pesticides and plastic additives affect gene expression and biological pathways. The tool identifies Adverse Outcome Pathways and network modules impacted by these chemicals using knowledge graphs and literature mining.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.