TY - JOUR

T1 - A deep generative model enables automated structure elucidation of novel psychoactive substances

AU - Skinnider, Michael A.

AU - Wang, Fei

AU - Pasin, Daniel

AU - Greiner, Russell

AU - Foster, Leonard J.

AU - Dalsgaard, Petur W.

AU - Wishart, David S.

N1 - Funding Information: This work was supported by funding from Genome Canada, Genome British Columbia and Genome Alberta (project 284MBO), the National Institutes of Health (NIH), National Institute of Environmental Health Sciences grant no. U2CES030170 and computational resources provided by WestGrid, Compute Canada and Advanced Research Computing at the University of British Columbia. M.A.S. acknowledges support from a CIHR Vanier Canada Graduate Scholarship, a Roman M. Babicki Fellowship in Medical Research, a Borealis AI Graduate Fellowship, a Walter C. Sumner Memorial Fellowship and a Vancouver Coastal Health–CIHR–UBC MD/PhD Studentship. Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer Nature Limited.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.1038/s42256-021-00407-x

DO - 10.1038/s42256-021-00407-x

M3 - Journal article

AN - SCOPUS:85119061562

VL - 3

SP - 973

EP - 984

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

SN - 2522-5839

IS - 11

ER -