A deep generative model enables automated structure elucidation of novel psychoactive substances

Skinnider, MA; Wang, F; Pasin, D; Greiner, R; Foster, LJ; Dalsgaard, PW; Wishart, DS

A deep generative model enables automated structure elucidation of novel psychoactive substances

Skinnider, MA Wang, F Pasin, D

Greiner, R Foster, LJ Dalsgaard, PW Wishart, DS

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Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Nature Machine Intelligence, 2021, 3, (11), pp. 973-984
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Skinnider, MA
dc.contributor.author	Wang, F
dc.contributor.author	Pasin, D https://orcid.org/0000-0002-5037-7290
dc.contributor.author	Greiner, R
dc.contributor.author	Foster, LJ
dc.contributor.author	Dalsgaard, PW
dc.contributor.author	Wishart, DS
dc.date.accessioned	2022-10-26T20:09:32Z
dc.date.available	2022-10-26T20:09:32Z
dc.date.issued	2021-11-01
dc.identifier.citation	Nature Machine Intelligence, 2021, 3, (11), pp. 973-984
dc.identifier.issn	2522-5839
dc.identifier.issn	2522-5839
dc.identifier.uri	http://hdl.handle.net/10453/162726
dc.description.abstract	Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.
dc.language	English
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Nature Machine Intelligence
dc.relation.isbasedon	10.1038/s42256-021-00407-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A deep generative model enables automated structure elucidation of novel psychoactive substances
dc.type	Journal Article
utslib.citation.volume	3
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-26T20:09:28Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	3
utslib.citation.issue	11

Abstract:

Over the past decade, the illicit drug market has been reshaped by the proliferation of clandestinely produced designer drugs. These agents, referred to as new psychoactive substances (NPSs), are designed to mimic the physiological actions of better-known drugs of abuse while skirting drug control laws. The public health burden of NPS abuse obliges toxicological, police and customs laboratories to screen for them in law enforcement seizures and biological samples. However, the identification of emerging NPSs is challenging due to the chemical diversity of these substances and the fleeting nature of their appearance on the illicit market. Here we present DarkNPS, a deep learning-enabled approach to automatically elucidate the structures of unidentified designer drugs using only mass spectrometric data. Our method employs a deep generative model to learn a statistical probability distribution over unobserved structures, which we term the structural prior. We show that the structural prior allows DarkNPS to elucidate the exact chemical structure of an unidentified NPS with an accuracy of 51% and a top-10 accuracy of 86%. Our generative approach has the potential to enable de novo structure elucidation for other types of small molecules that are routinely analysed by mass spectrometry.

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http://hdl.handle.net/10453/162726