Researchers train computers to predict the next designer drugs
Global law enforcement agencies are already using the new method

Date:
November 15, 2021
Source:
University of British Columbia
Summary:
Researchers have trained computers to predict the next designer
drugs before they are even on the market, technology that could
save lives.

Identifying these so-called 'legal highs' within seized pills or
powders can take months, during which time thousands of people
may have already used a new designer drug. But new research is
already helping law enforcement agencies around the world to cut
identification time down from months to days, crucial in the race to
identify and regulate new versions of dangerous psychoactive drugs.



FULL STORY ==========================================================================
UBC researchers have trained computers to predict the next designer drugs before they are even on the market, technology that could save lives.


==========================================================================
Law enforcement agencies are in a race to identify and regulate new
versions of dangerous psychoactive drugs such as bath salts and synthetic opioids, even as clandestine chemists work to synthesize and distribute
new molecules with the same psychoactive effects as classical drugs
of abuse.

Identifying these so-called "legal highs" within seized pills or powders
can take months, during which time thousands of people may have already
used a new designer drug.

But new research is already helping law enforcement agencies around the
world to cut identification time down from months to days, crucial in
the race to identify and regulate new versions of dangerous psychoactive
drugs.

"The vast majority of these designer drugs have never been tested in
humans and are completely unregulated. They are a major public health
concern to emergency departments across the world," says UBC medical
student Dr. Michael Skinnider, who completed the research as a doctoral
student at UBC's Michael Smith Laboratories.

A Minority Report for new designer drugs Dr. Skinnider and his colleagues
used a database of known psychoactive substances contributed by forensic laboratories around the world to train an artificial intelligence
algorithm on the structures of these drugs. The algorithm they used,
known as a deep neural network, is inspired by the structure and function
of the human brain.



========================================================================== Based on this training, the model then generated about 8.9 million
potential designer drugs.

These molecules were then tested against 196 new designer drugs that
emerged on the illicit market after the model was trained. The researchers found more than 90 per cent were present in the generated set.

In other words, the model was able to predict nearly all of the new
drugs discovered since it was trained.

"The fact that we can predict what designer drugs are likely to emerge
on the market before they actually appear is a bit like the 2002 sci-fi
movie, Minority Report, where foreknowledge about criminal activities
about to take place helped significantly reduce crime in a future
world," explains senior author Dr. David Wishart (he/him), a professor
of computing science at the University of Alberta.

"Essentially, our software gives law enforcement agencies and public
health programs a head start on the clandestine chemists, and lets them
know what to be on the lookout for." Identification in days instead
of months


==========================================================================
This still left the problem of how to easily identify completely unknown substances.

The researchers found the model had also learned which molecules were more likely to appear on the market, and which were less likely. "We wondered whether we could use this probability to determine what an unknown drug
is - - based solely on its mass -- which is easy for a chemist to measure
for any pill or powder using mass spectrometry," says Dr. Leonard Foster (he/him), a professor in the department of biochemistry at UBC and an internationally recognized expert on mass spectrometry.

The researchers tested this hypothesis using each of the 196 new designer drugs. Using only the mass, the researchers found their model ranked
the correct chemical structure of an unidentified designer drug among
the top 10 candidates 72 per cent of the time. Integrating tandem mass spectrometry data, another easily obtained measurement, improved this
to 86 per cent. When it came to just one guess, the model could predict
the correct structure 51 per cent of the time.

"It was shocking to us that the model performed this well, because
elucidating entire chemical structures from just an accurate mass
measurement is generally thought to be an unsolvable problem. And
narrowing down a list of billions of structures to a set of 10 candidates
could massively accelerate the pace at which new designer drugs can be identified by chemists," notes Dr. Skinnider.

The same kind of model could be used to discover all kinds of new
molecules, adds Dr. Skinnider, from identifying new performance-enhancing
drugs for athletic doping, to identifying previously unknown molecules
in human blood and urine.

"There is an entire world of chemical 'dark matter' just beyond our
fingertips right now. I think there is a huge opportunity for the
right AI tools to shine a light on this unknown chemical world," says
Dr. Skinnider.

Distributed securely by the Novel Psychoactive Substance Data Hub, the
UBC model is being used by the US Drug Enforcement Agency, the United
Nations Office of Drugs and Crime, the European Monitoring Centre of
Drugs and Drug Addiction, and Federal Criminal Police Office of Germany.

========================================================================== Story Source: Materials provided by University_of_British_Columbia. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Skinnider, M.A., Wang, F., Pasin, D. et al. A deep generative model
enables automated structure elucidation of novel psychoactive
substances.

Nat Mach Intell, 2021 DOI: 10.1038/s42256-021-00407-x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211115123541.htm

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