ArticleNature chemical biology2022
Artificial intelligence uncovers carcinogenic human metabolites.
Article in Nature chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- MutAIverse: an AI-powered, mechanism-backed platform for discovering novel DNA adducts and their precursor genotoxins.Journal of cheminformatics · 2026Article
- Metabolic networks in the tumor microenvironment: roles of amino acid and lipid metabolism pathways in cancer progression and therapy.Experimental & molecular medicine · 2026Review
- Deep learning reveals endogenous sterols as allosteric modulators of the GPCR-Gα interface.eLife · 2025Article
- Review
- Transforming beef quality through healthy breeding: a strategy to reduce carcinogenic compounds and enhance human health: a review.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Emerging Trends in Machine Learning: A Polymer Perspective.ACS polymers Au · 2023Review
- Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.