دورية أكاديمية
Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances
| العنوان: | Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances |
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| المؤلفون: | Fei Wang, Daniel Pasin, Michael A. Skinnider, Jaanus Liigand, Jan-Niklas Kleis, David Brown, Eponine Oler, Tanvir Sajed, Vasuk Gautam, Stephen Harrison, Russell Greiner, Leonard J. Foster, Petur Weihe Dalsgaard, David S. Wishart |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Biophysics, Biochemistry, Medicine, Neuroscience, Pharmacology, Sociology, Immunology, Infectious Diseases, Space Science, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, unknown powder seized, toxicological laboratories tasked, tandem mass spectrometry, large data set, chemical structures alone, primary method used, novel psychoactive substances, recently emerged nps, newly emerging nps, https :// nps, ms prediction model, experimentally acquired ms, allow forensic laboratories, given nps compounds, ms spectra identification, ms reference standards, reference standards, ms spectra, novel derivative |
| الوصف: | The market for illicit drugs has been reshaped by the emergence of more than 1100 new psychoactive substances (NPS) over the past decade, posing a major challenge to the forensic and toxicological laboratories tasked with detecting and identifying them. Tandem mass spectrometry (MS/MS) is the primary method used to screen for NPS within seized materials or biological samples. The most contemporary workflows necessitate labor-intensive and expensive MS/MS reference standards, which may not be available for recently emerged NPS on the illicit market. Here, we present NPS-MS, a deep learning method capable of accurately predicting the MS/MS spectra of known and hypothesized NPS from their chemical structures alone. NPS-MS is trained by transfer learning from a generic MS/MS prediction model on a large data set of MS/MS spectra. We show that this approach enables a more accurate identification of NPS from experimentally acquired MS/MS spectra than any existing method. We demonstrate the application of NPS-MS to identify a novel derivative of phencyclidine (PCP) within an unknown powder seized in Denmark without the use of any reference standards. We anticipate that NPS-MS will allow forensic laboratories to identify more rapidly both known and newly emerging NPS. NPS-MS is available as a web server at https://nps-ms.caTest/, which provides MS/MS spectra prediction capabilities for given NPS compounds. Additionally, it offers MS/MS spectra identification against a vast database comprising approximately 8.7 million predicted NPS compounds from DarkNPS and 24.5 million predicted ESI-QToF-MS/MS spectra for these compounds. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| العلاقة: | https://figshare.com/articles/journal_contribution/Deep_Learning-Enabled_MS_MS_Spectrum_Prediction_Facilitates_Automated_Identification_Of_Novel_Psychoactive_Substances/24728704Test |
| DOI: | 10.1021/acs.analchem.3c02413.s001 |
| الإتاحة: | https://doi.org/10.1021/acs.analchem.3c02413.s001Test https://figshare.com/articles/journal_contribution/Deep_Learning-Enabled_MS_MS_Spectrum_Prediction_Facilitates_Automated_Identification_Of_Novel_Psychoactive_Substances/24728704Test |
| حقوق: | CC BY-NC 4.0 |
| رقم الانضمام: | edsbas.DCD91C5F |
| قاعدة البيانات: | BASE |
| DOI: | 10.1021/acs.analchem.3c02413.s001 |
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