Global law enforcement agencies are already using the new method

UBC researchers have trained computers to predict upcoming designer drugs before they even hit the market, a technology that could save lives.

Law enforcement is in a race to identify and regulate new versions of dangerous psychoactive drugs such as bath salts and synthetic opioids, even as clandestine chemists scramble to synthesize and distribute new molecules with the same psychoactive effects as conventional drugs.

Identifying these so-called “legal highs” in 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 around the world cut identification time from months to days, which is crucial in the race to identify and regulate new versions of psychoactive drugs. dangerous.

“The vast majority of these designer drugs have never been tested on humans and are completely unregulated. They are a major public health concern for emergency services around the world,” says Dr. Michael Skinnider, medical student at UBC, who completed the research as a doctoral student. student at UBC’s Michael Smith Laboratories.

A Minority report for new synthetic drugs

Dr. Skinnider and his colleagues used a database of known psychoactive substances provided 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 approximately 8.9 million potential designer drugs.

These molecules were then tested against 196 new synthetic drugs that appeared on the illicit market after training the model. The researchers found that more than 90% were present in the generated set.

In other words, the model was able to predict almost every new drug discovered since its formation.

“The fact that we can predict which designer drugs are likely to hit the market before they actually appear is a bit like the 2002 sci-fi movie, Minority reportwhere foreknowledge of criminal activity about to take place has helped to dramatically reduce crime in a future world,” says lead author Dr. David Wishart (he she), professor of computer science at the University of Alberta.

“Essentially, our software gives law enforcement and public health programs a head start on clandestine chemists, and lets them know what to watch out for.”

Identification in days instead of months

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

The researchers found that the model also learned which molecules were more likely to appear on the market and which were less likely. “We wondered if 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. “, explains Dr. Leonard Foster (he she), a professor in UBC’s Department of Biochemistry and an internationally recognized expert in mass spectrometry.

The researchers tested this hypothesis using each of the 196 new designer drugs. Using only mass, the researchers found that their model ranked the correct chemical structure of an unidentified designer drug among the top 10 candidates 72% of the time. Integrating tandem mass spectrometry data, another easily obtained metric, improved that figure to 86%. When it was a single guess, the model could predict the correct structure 51% of the time.

“It was shocking to us that the model performed so well, because elucidating entire chemical structures from a single accurate mass measurement is generally considered an insoluble problem. And reducing a list of billions of structures to a set of 10 candidates could massively accelerate the rate 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 sports doping to identifying previously unknown molecules in blood and blood. human urine.

“There’s a whole world of chemical ‘dark matter’ right beyond our fingertips right now. I think there’s 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 used by the United States Drug Enforcement Agency, United Nations Office on Drugs and Crime, European Monitoring Center for Drugs and Drug Addiction and the Federal Criminal Police Office of Germany.