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Xenobiotics

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XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.

NAR genomics and bioinformatics
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model.

Ecotoxicology and environmental safety
Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the ...

Assessing chemical exposure risk in breastfeeding infants: An explainable machine learning model for human milk transfer prediction.

Ecotoxicology and environmental safety
Breast milk is essential for infant health, but the transfer of xenobiotic chemicals poses significant risks. Ethical challenges in clinical trials necessitate the use of in vitro predictive models to assess chemical exposure risks in breastfeeding i...

Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties.

Archives of toxicology
Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabo...

Computational prediction of the metabolites of agrochemicals formed in rats.

The Science of the total environment
Today, computational tools for the prediction of the metabolite structures of xenobiotics are widely available and employed in small-molecule research. Reflecting the availability of measured data, these in silico tools are trained and validated prim...

GutBug: A Tool for Prediction of Human Gut Bacteria Mediated Biotransformation of Biotic and Xenobiotic Molecules Using Machine Learning.

Journal of molecular biology
Dietary components and bioactive molecules present in functional foods and nutraceuticals provide various beneficial effects including modulation of host gut microbiome. These metabolites along with orally administered drugs can be potentially bio-tr...

Applying machine learning techniques for ADME-Tox prediction: a review.

Expert opinion on drug metabolism & toxicology
INTRODUCTION: Pharmacokinetics involves the study of absorption, distribution, metabolism, excretion and toxicity of xenobiotics (ADME-Tox). In this sense, the ADME-Tox profile of a bioactive compound can impact its efficacy and safety. Moreover, eff...

Dietary Inhibitors of CYP3A4 Are Revealed Using Virtual Screening by Using a New Deep-Learning Classifier.

Journal of agricultural and food chemistry
CYP3A4 is the main human enzyme responsible for phase I metabolism of dietary compounds, prescribed drugs and xenobiotics, steroid hormones, and bile acids. The inhibition of CYP3A4 activity might impair physiological mechanisms, including the endocr...

CYPstrate: A Set of Machine Learning Models for the Accurate Classification of Cytochrome P450 Enzyme Substrates and Non-Substrates.

Molecules (Basel, Switzerland)
The interaction of small organic molecules such as drugs, agrochemicals, and cosmetics with cytochrome P450 enzymes (CYPs) can lead to substantial changes in the bioavailability of active substances and hence consequences with respect to pharmacologi...