Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

Wang, F; Pasin, D; Skinnider, MA; Liigand, J; Kleis, J-N; Brown, D; Oler, E; Sajed, T; Gautam, V; Harrison, S; Greiner, R; Foster, LJ; Dalsgaard, PW; Wishart, DS

Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.

Wang, F Pasin, D

Skinnider, MA Liigand, J Kleis, J-N Brown, D Oler, E Sajed, T Gautam, V Harrison, S Greiner, R Foster, LJ Dalsgaard, PW Wishart, DS

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Publisher:: AMER CHEMICAL SOC
Publication Type:: Journal Article
Citation:: Anal Chem, 2023, 95, (50), pp. 18326-18334
Issue Date:: 2023-12-19

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Field	Value	Language
dc.contributor.author	Wang, F
dc.contributor.author	Pasin, D https://orcid.org/0000-0002-5037-7290
dc.contributor.author	Skinnider, MA
dc.contributor.author	Liigand, J
dc.contributor.author	Kleis, J-N
dc.contributor.author	Brown, D
dc.contributor.author	Oler, E
dc.contributor.author	Sajed, T
dc.contributor.author	Gautam, V
dc.contributor.author	Harrison, S
dc.contributor.author	Greiner, R
dc.contributor.author	Foster, LJ
dc.contributor.author	Dalsgaard, PW
dc.contributor.author	Wishart, DS
dc.date.accessioned	2024-03-19T02:00:59Z
dc.date.available	2024-03-19T02:00:59Z
dc.date.issued	2023-12-19
dc.identifier.citation	Anal Chem, 2023, 95, (50), pp. 18326-18334
dc.identifier.issn	0003-2700
dc.identifier.issn	1520-6882
dc.identifier.uri	http://hdl.handle.net/10453/176895
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	AMER CHEMICAL SOC
dc.relation.ispartof	Anal Chem
dc.relation.isbasedon	10.1021/acs.analchem.3c02413
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0399 Other Chemical Sciences
dc.subject.classification	Analytical Chemistry
dc.subject.classification	3205 Medical biochemistry and metabolomics
dc.subject.classification	3401 Analytical chemistry
dc.subject.classification	4004 Chemical engineering
dc.subject.mesh	Tandem Mass Spectrometry
dc.subject.mesh	Deep Learning
dc.subject.mesh	Psychotropic Drugs
dc.subject.mesh	Illicit Drugs
dc.subject.mesh	Spectrometry, Mass, Electrospray Ionization
dc.subject.mesh	Psychotropic Drugs
dc.subject.mesh	Spectrometry, Mass, Electrospray Ionization
dc.subject.mesh	Tandem Mass Spectrometry
dc.subject.mesh	Deep Learning
dc.subject.mesh	Illicit Drugs
dc.subject.mesh	Tandem Mass Spectrometry
dc.subject.mesh	Deep Learning
dc.subject.mesh	Psychotropic Drugs
dc.subject.mesh	Illicit Drugs
dc.subject.mesh	Spectrometry, Mass, Electrospray Ionization
dc.title	Deep Learning-Enabled MS/MS Spectrum Prediction Facilitates Automated Identification Of Novel Psychoactive Substances.
dc.type	Journal Article
utslib.citation.volume	95
utslib.location.activity	United States
utslib.for	0301 Analytical Chemistry
utslib.for	0399 Other Chemical Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
utslib.copyright.status	open_access	*
dc.date.updated	2024-03-19T02:00:57Z
pubs.issue	50
pubs.publication-status	Published
pubs.volume	95
utslib.citation.issue	50

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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http://hdl.handle.net/10453/176895