posted on 2023-12-04, 20:00authored byFei 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
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.