Machine learning– and multilayer molecular network–assisted screening hunts fentanyl compounds Science Advances Fentanyl and its analogs are a global concern, making their accurate identification essential for public health. This article introduces Fentanyl-Hunter, a screening platform that uses a machine learning classifier and multilayer molecular network that covers more than 87% of known fentanyls to select and annotate fentanyl compounds using mass spectrometry (MS). Fentanyl-Hunter identified fentanyl members in biological and environmental samples. During biotransformation, 35 metabolites from four widely consumed fentanyl derivatives were identified. Norfentanyl was the major fentanyl compound in wastewater. Retrospective screening of these biomarkers across more than 605,000 MS files in public datasets revealed fentanyl, sufentanil, norfentanyl, or remifentanil acid in more than 250 samples from eight major countries, indicating the potential widespread presence of fentanyl. Read this issue of the ASAM Weekly Subscribe to the ASAM Weekly Visit ASAM

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