ArticleAnalytical chemistry2023
Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.
Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 29 citations in OpenAlex.
- Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026Review
- A Novel Workflow for Fast Elucidation of Drug Metabolites for Screening-Combining In Silico Metabolite Prediction With Trapped Ion Mobility QTOF-MS.Drug testing and analysis · 2026Article
- Comprehensive Profiling of Human Urinary Mercapturic Acid Conjugates Associated with Exposure to Reactive Chemical Species Using Enzymatic Deacetylation and High-Resolution Mass Spectrometry.Analytical chemistry · 2026Article
- Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by Transfer Learning-Based Neural Network.Environmental science & technology · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Graph Machine Learning Can Estimate Drug Concentrations in Whole Blood from Forensic Screening Results.Analytical chemistry · 2026Article
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- Review
- Large-scale generation of in silico based spectral libraries to annotate dark chemical space features in non-target analysis.Analytical and bioanalytical chemistry · 2025Article
- Metabolome informs about the chemical exposome and links to brain health.Environment international · 2025Review
- Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.Analytical and bioanalytical chemistry · 2025Review
- Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules.Nature communications · 2024Article
- Accurate Prediction ofMetabolites · 2024Article
- Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.Environmental science & technology · 2024Article
- PSAnalytical chemistry · 2024Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.ca/, 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.
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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.